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Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
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Other Emotions
References
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Measuring the Happiness, Health, andStories of Populations
Last updated: 2019/01/14, 22:05:08
Complex Networks | @networksvoxCSYS/MATH 303, Spring, 2019
Prof. Peter Dodds | @peterdodds
Dept. of Mathematics & Statistics | Vermont Complex Systems CenterVermont Advanced Computing Core | University of Vermont
Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.2 of 155
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Other Emotions
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Happiness
Introduction
Measurement
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Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
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Outline
Introduction
Measurement
HappinessSome motivationMeasuring emotional contentHedonometerAnalysisSongsBlogsSOTUGeographyMovementOther Emotions
References
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
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Key papers:ar
Xiv
:0909.1
104v1 [n
lin.P
S]
6 S
ep 2
009
On the optimal form of branching supply and collection networks
Peter Sheridan Dodds1, ∗
1 Department of Mathematics and Statistics, Center for Complex Systems,
& the Vermont Advanced Computing Center, University of Vermont, Burlington, VT, 05401
(Dated: September 6, 2009)
For the problem of efficiently supplying material to a spatial region from a single source, wepresent a simple scaling argument based on branching network volume minimization that identifieslimits to the scaling of sink density. We discuss implications for two fundamental and unresolvedproblems in organismal biology and geomorphology: how basal metabolism scales with body sizefor homeotherms and the scaling of drainage basin shape on eroding landscapes.
PACS numbers: 89.75.Fb,89.75.Hc,87.19.U-,92.40.Gc
In both natural and man-made systems, branching net-works universally facilitate the essential task of supplyingmaterial from a central source to a widely distributedsink population. Branching networks also underlie thecomplementary process of collecting material from manysources at a single sink. Such networks typically exhib-it structural self-similarity over many orders of magni-tude: river networks drain continents [1, 2, 3], arterialand venal networks move blood between the macroscopicheart and microscopic capillaries [4], and trees and plantsorient leaves in space taking on the roles of both structureand transportation.
We address the following questions regarding supplynetworks: (1) What is the minimum network volumerequired to continually supply material from a source toa population of sinks in some spatial region Ω?; and (2)How does this optimal solution scale if Ω is rescaled allo-metrically? (For convenience, we use the language of dis-tribution, i.e., a single source supplying many sinks.) Ourapproach is inspired by that of Banavar et al. [5, 6] whosought to derive scaling properties of optimal transporta-tion networks in isometrically growing regions based ona flow rate argument; Banavar et al.’s approach followedthe seminal work of West et al. [7] who suggested supplynetworks were key to understanding the metabolic limi-tations of organisms, and focused on network impedanceminimization (see [8, 9]). In contrast to this previ-ous work, our treatment is explicitly geometric. Wealso accommodate four other key features: the ambientdimension, allometrically growing regions, variable sinkdensity, and varying speed of material transportation.
We consider the problem of network supply for a gen-eral class of d-dimensional spatial regions in a D ≥ ddimensional space. Each region Ω has volume V andoverall dimensions L1 × L2 × · · · × Ld (see Fig. 1a). Weallow these length scales to scale as Li ∝ V γi , creatingfamilies of allometrically similar regions. For isometricgrowth, all dimensions scale uniformly meaning γi = 1/d,while for allometric growth, we must have at least one ofthe γi being different (Fig. 1b). For the general caseof allometry, we choose an ordering of γi such that thelength scales are arranged from most dominant to least
b.
L′1L1
Ω(V )L2 L′2
Ω(V ′)a.
2rsink
s
2rsource
c.
FIG. 1: (a) We consider families of d-dimensional spatialregions that scale allometrically with Li ∝ V γi , and exist in aD-dimensional space where D ≥ d. For the d=D=2 exampleshown, γmax = γ1 > γ2, and L1 grows faster than L2. Werequire that each spatial region is star-convex, i.e., from atleast one point all other points are directly observable, andthe single source must be located at any one of these cen-tral points. (b) Distribution (or collection) networks can bethought of as a superposition of virtual vessels. In the exam-ple shown, the source (circle) supplies material to the threesinks (squares). (c) Allowing virtual vessels to expand as theymove away from the source captures a potential decrease inspeed in material flow. For scaling of branching network formto be affected, the radius r of a virtual vessel must scale withvessel length s (measured from the sink) as s−ε.
dominant: γmax = γ1 ≥ . . . ≥ γd.
We assume that isolated sinks are located throughouta contiguous spatial region Ω (volume V ) which containsa single source located at "x = "0. We allow sink den-sity to follow ρ ∼ ρ0(V )(1 + a||"x||)−ζ where a is fixed,ζ ≥ 0, and ||"x|| is the distance from the source. Whenthe exponent ζ = 0, ρ is constant throughout the region(as for capillaries in organisms), but remains a functionof the region’s overall volume V . While decreasing sink
Typeset by REVTEX
“Measuring the happiness of large-scale writtenexpression: Songs, blogs, and presidents.”, Doddsand Danforth, Journal of Happiness Studies, 11,441–456, 2009. [9]
Temporal Patterns of Happiness and Information in aGlobal Social Network: Hedonometrics and Twitter
Peter Sheridan Dodds1,2,3*, Kameron Decker Harris1, Isabel M. Kloumann1,4, Catherine A. Bliss1,
Christopher M. Danforth1,2,3*
1Department of Mathematics and Statistics, University of Vermont, Burlington, Vermont, United States of America, 2Center for Complex Systems, University of Vermont,
Burlington, Vermont, United States of America, 3 Vermont Advanced Computing Center, University of Vermont, Burlington, Vermont, United States of America,
4Department of Physics, University of Vermont, Burlington, Vermont, United States of America
Abstract
Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often beenindirectly characterized and overshadowed by more readily quantifiable economic indicators such as gross domesticproduct. Here, we examine expressions made on the online, global microblog and social networking service Twitter,uncovering and explaining temporal variations in happiness and information levels over timescales ranging from hours toyears. Our data set comprises over 46 billion words contained in nearly 4.6 billion expressions posted over a 33 month spanby over 63 million unique users. In measuring happiness, we construct a tunable, real-time, remote-sensing, and non-invasive, text-based hedonometer. In building our metric, made available with this paper, we conducted a survey to obtainhappiness evaluations of over 10,000 individual words, representing a tenfold size improvement over similar existing wordsets. Rather than being ad hoc, our word list is chosen solely by frequency of usage, and we show how a highly robust andtunable metric can be constructed and defended.
Citation: Dodds PS, Harris KD, Kloumann IM, Bliss CA, Danforth CM (2011) Temporal Patterns of Happiness and Information in a Global Social Network:Hedonometrics and Twitter. PLoS ONE 6(12): e26752. doi:10.1371/journal.pone.0026752
Editor: Johan Bollen, Indiana University – Bloomington, United States of America
Received January 15, 2011; Accepted October 3, 2011; Published December 7, 2011
Copyright: ! 2011 Dodds et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors are grateful for the computational resources provided by the Vermont Advanced Computing Center which is supported by NASA (NNX08A096G). KDH was supported by VT-NASA EPSCoR. PSD was supported by NSF CAREER Award # 0846668. The funders had no role in study design, datacollection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: peter.dodds@uvm.edu (PSD); chris.danforth@uvm.edu (CMD)
Introduction
One of the great modern scientific challenges we face lies in
understanding macroscale sociotechnical phenomena–i.e., the
behavior of decentralized, networked systems inextricably involv-
ing people, information, and machine algorithms–such as global
economic crashes and the spreading of ideas and beliefs [1].
Accurate description through quantitative measurement is essen-
tial to the advancement of any scientific field, and the shift from
being data scarce to data rich has revolutionized many areas [2–5]
ranging from astronomy [6–8] to ecology and biology [9] to
particle physics [10]. For the social sciences, the now widespread
usage of the Internet has led to a collective, open recording of an
enormous number of transactions, interactions, and expressions,
marking a clear transition in our ability to quantitatively
characterize, and thereby potentially understand, previously
hidden as well as novel microscale mechanisms underlying
sociotechnical systems [11].
While there are undoubtedly limits to that which may
eventually be quantified regarding human behavior, recent studies
have demonstrated a number of successful and diverse method-
ologies, all impossible (if imaginable) prior to the Internet age.
Three examples relevant to public health, markets, entertainment,
history, evolution of language and culture, and prediction are (1)
Google’s digitization of over 15 million books and an initial
analysis of the last two hundred years, showing language usage
changes, censorship, dynamics of fame, and time compression of
collective memory [12,13]; (2) Google’s Flu Trends [14–16] which
allows for real-time monitoring of flu outbreaks through the proxy
of user search; and (3) the accurate prediction of box office success
based on the rate of online mentions of individual movies [17] (see
also [18]).
Out of the many possibilities in the ‘Big Data’ age of social
sciences, we focus here on measuring, describing, and under-
standing the well-being of large populations. A measure of ‘societal
happiness’ is a crucial adjunct to traditional economic measures
such as gross domestic product and is of fundamental scientific
interest in its own right [19–22].
Our overall objective is to use web-scale text analysis to
remotely sense societal-scale levels of happiness using the singular
source of the microblog and social networking service Twitter.
Our contributions are both methodological and observational.
First, our method for measuring the happiness of a given text,
which we introduced in [23] and which we improve upon greatly
in the present work, entails word frequency distributions combined
with independently assessed numerical estimates of the ‘happiness’
of over 10,000 words obtained using Amazon’s Mechanical Turk
[24]. We describe our method in full below and demonstrate its
robustness. We refer to our data set as ‘language assessment by
Mechanical Turk 1.0’, which abbreviates as labMT 1.0, and we
provide all data as Data Set S1.
Second, using Twitter as a data source, we are able to explore
happiness as a function of time, space, demographics, and network
structure, with time being our focus here. Twitter is extremely
PLoS ONE | www.plosone.org 1 December 2011 | Volume 6 | Issue 12 | e26752
“Temporal patterns of happiness andinformation in a global social network:Hedonometrics and Twitter”Dodds et al.,PLoS ONE, 6, e26752, 2011. [11]
Positivity of the English Language
Isabel M. Kloumann1,4, Christopher M. Danforth1,2,3*, Kameron Decker Harris1, Catherine A. Bliss1, Peter
Sheridan Dodds1,2,3*
1Department of Mathematics and Statistics, University of Vermont, Burlington, Vermont, United States of America, 2Center for Complex Systems, University of Vermont,
Burlington, Vermont, United States of America, 3 Vermont Advanced Computing Center, University of Vermont, Burlington, Vermont, United States of America,
4Department of Physics, University of Vermont, Burlington, Vermont, United States of America
Abstract
Over the last million years, human language has emerged and evolved as a fundamental instrument of socialcommunication and semiotic representation. People use language in part to convey emotional information, leading to thecentral and contingent questions: (1) What is the emotional spectrum of natural language? and (2) Are natural languagesneutrally, positively, or negatively biased? Here, we report that the human-perceived positivity of over 10,000 of the mostfrequently used English words exhibits a clear positive bias. More deeply, we characterize and quantify distributions of wordpositivity for four large and distinct corpora, demonstrating that their form is broadly invariant with respect to frequency ofword use.
Citation: Kloumann IM, Danforth CM, Harris KD, Bliss CA, Dodds PS (2012) Positivity of the English Language. PLoS ONE 7(1): e29484. doi:10.1371/journal.pone.0029484
Editor: Luıs A. Nunes Amaral, Northwestern University, United States of America
Received October 26, 2011; Accepted November 29, 2011; Published January 11, 2012
Copyright: ! 2012 Kloumann et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors are grateful for the computational resources provided by the Vermont Advanced Computing Center which is supported by NASA (NNX08A096G). KDH was supported by VT-NASA EPSCoR. PSD was supported by NSF CAREER Award # 0846668. The funders had no role in study design, datacollection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: peter.dodds@uvm.edu (PSD); chris.danforth@uvm.edu (CMD)
Introduction
While we regard ourselves as social animals, we have a history of
actions running from selfless benevolence to extreme violence at all
scales of society, and we remain scientifically and philosophically
unsure as to what degree any individual or group is or should be
cooperative and pro-social. Traditional economic theory of human
behavior, for example, assumes that people are inherently and
rationally selfish–a core attribute of homo economicus–with the
emergence of global cooperation thus rendered a profound mystery
[1,2]. Yet everyday experience and many findings of psychology,
behavioral economics, and neuroscience indicate people favour
seemingly irrational heuristics [3,4] over strict rationality as
exemplified in loss-aversion [5], confirmation bias [6], and altruistic
punishment [7]. Religions and philosophies similarly run the gamut
in prescribing the right way for individuals to behave, from the
universal non-harming advocated by Jainism, Gandhi’s call for non-
violent collective resistance, and exhortations toward altruistic
behavior in all major religions, to arguments for the necessity of a
Monarch [8], the strongest forms of libertarianism, and the
‘‘rational self-interest’’ of Ayn Rand’s Objectivism [9].
In taking the view that humans are in part story-tellers–homo
narrativus–we can look to language itself for quantifiable evidence of
our social nature. How is the structure of the emotional content
rendered in our stories, fact or fiction, and social interactions
reflected in the collective, evolutionary construction of human
language? Previous findings are mixed: suggestive evidence of a
positive bias has been found in small samples of English words
[10–12], framed as the Pollyanna Hypothesis [10] and Linguistic
Positivity Bias [12], while experimental elicitation of emotional
words has instead found a strong negative bias [13].
To test the overall positivity of the English language, and in
contrast to previous work [11,13,14], we chose words based solely
on frequency of use, the simplest and most impartial gauge of word
importance. We focused on measuring happiness, or psychological
valence [15], as it represents the dominant emotional response
[16,17]. With this approach, we examined four large-scale text
corpora (see Tab. 1 for details): Twitter, The Google Books Project
(English), The New York Times, and Music lyrics. These corpora,
which we will refer to as TW, GB, NYT, and ML, cover a wide
range of written expression including broadcast media, opinion,
literature, songs, and public social interactions ([18]), and span the
gamut in terms of grammatical and orthographic correctness.
We took the top 5000 most frequently used words from each
corpus, and merged them to form a resultant list of 10,222 unique
words. We then used Amazon’s Mechanical Turk [19,20] to
obtain 50 independent evaluations per word on a 1 to 9 integer
scale, asking participants to rate their happiness in response to
each word in isolation (1 = least happy, 5= neutral, and 9=most
happy [14,21]). While still evolving, Mechanical Turk has proved
over the last few years to be a reliable and fast service for carrying
out large-scale social science research [22–26].
We computed the average happiness score and standard
deviation for each word. We obtained sensible results that showed
excellent statistical agreement with previous studies for smaller
word sets, including a translated Spanish version (see [14,20,
27] for details). The highest and lowest scores were
havg(‘laughter’)=8.50 and havg(‘terrorist’)=1.30, with expectedly
neutral words averaging near 5, e.g., havg(‘the’)=4.98 and
havg(‘it’)=5.02. We refer to our ongoing studies as Language
Assessment by Mechanical Turk, using the abbreviation labMT
1.0 data set for the present work (the full data set is provided as
PLoS ONE | www.plosone.org 1 January 2012 | Volume 7 | Issue 1 | e29484
“Positivity of the English language”, Kloumann etal., PLoS ONE, 7, e29484, 2012. [23]
Reagan et al. EPJ Data Science ( 2017) 6:28
DOI 10.1140/epjds/s13688-017-0121-9
REGULAR ART ICLE Open Access
Sentiment analysis methods forunderstanding large-scale texts: a case forusing continuum-scored words and wordshift graphsAndrew J Reagan1*, Christopher M Danforth1, Brian Tivnan2, Jake Ryland Williams3 and
Peter Sheridan Dodds1
*Correspondence:
andy@andyreagan.com1University of Vermont, 85 South
Prospect St, Burlington, VT 05405,
USA
Full list of author information is
available at the end of the article
Abstract
The emergence and global adoption of social media has rendered possible the
real-time estimation of population-scale sentiment, an extraordinary capacity which
has profound implications for our understanding of human behavior. Given the
growing assortment of sentiment-measuring instruments, it is imperative to
understand which aspects of sentiment dictionaries contribute to both their
classification accuracy and their ability to provide richer understanding of texts. Here,
we perform detailed, quantitative tests and qualitative assessments of 6
dictionary-based methods applied to 4 different corpora, and briefly examine a
further 20 methods. We show that while inappropriate for sentences,
dictionary-based methods are generally robust in their classification accuracy for
longer texts. Most importantly they can aid understanding of texts with reliable and
meaningful word shift graphs if (1) the dictionary covers a sufficiently large portion of
a given text’s lexicon when weighted by word usage frequency; and (2) words are
scored on a continuous scale.
Keywords: sentiment; sentiment analysis; sentiment dictionaries; language; natural
language processing; data visualization; text visualization
1 Introduction
As we move further into what might be called the Sociotechnocene — with increasingly
more interactions, decisions, and impact being made by globally distributed people and
algorithms — the myriad human social dynamics that have shaped our history have be-
come far more visible and measurable than ever before. Of the many ways we are now
able to characterize social systems in microscopic detail, sentiment detection for pop-
ulations at all scales has become a prominent research arena. Attempts to leverage on-
line expression for sentiment mining include prediction of stock markets [–], assessing
responses to advertising, real-time monitoring of global happiness [], and measuring a
health-related quality of life []. The diverse set of instruments produced by this work
now provide indicators that help scientists understand collective behavior, inform pub-
lic policy makers, and, in industry, gauge the sentiment of public response to marketing
© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in anymedium, pro-vided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, andindicate if changes were made.
“Sentiment analysis methods for understandinglarge-scale texts: A case for using continuum-scoredwords and word shift graphs”, Reagan et al., EPJData Science, 6, , 2017. [31]
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.6 of 155
Economics, Schmeconomics
Alan Greenspan (September 18, 2007):
“I’ve been dealing with these big mathematicalmodels of forecasting the economy ...
If I could figure out a way to determinewhether or not people are more fearful orchanging to more euphoric,
I don’t need any of this other stuff.
I could forecast the economy better than anyway I know.”
http://wikipedia.org
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.6 of 155
Economics, Schmeconomics
Alan Greenspan (September 18, 2007):
“I’ve been dealing with these big mathematicalmodels of forecasting the economy ...
If I could figure out a way to determinewhether or not people are more fearful orchanging to more euphoric,
I don’t need any of this other stuff.
I could forecast the economy better than anyway I know.”
http://wikipedia.org
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.6 of 155
Economics, Schmeconomics
Alan Greenspan (September 18, 2007):
“I’ve been dealing with these big mathematicalmodels of forecasting the economy ...
If I could figure out a way to determinewhether or not people are more fearful orchanging to more euphoric,
I don’t need any of this other stuff.
I could forecast the economy better than anyway I know.”
http://wikipedia.org
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.6 of 155
Economics, Schmeconomics
Alan Greenspan (September 18, 2007):
“I’ve been dealing with these big mathematicalmodels of forecasting the economy ...
If I could figure out a way to determinewhether or not people are more fearful orchanging to more euphoric,
I don’t need any of this other stuff.
I could forecast the economy better than anyway I know.”
http://wikipedia.org
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.6 of 155
Economics, Schmeconomics
Alan Greenspan (September 18, 2007):
“I’ve been dealing with these big mathematicalmodels of forecasting the economy ...
If I could figure out a way to determinewhether or not people are more fearful orchanging to more euphoric,
I don’t need any of this other stuff.
I could forecast the economy better than anyway I know.” http://wikipedia.org
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.7 of 155
Economics, Schmeconomics
Greenspan continues:
“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years. I’m no better than I ever was,and nobody else is. Forecasting 50 years ago was as good or asbad as it is today. And the reason is that human nature hasn’tchanged. We can’t improve ourselves.”
Jon Stewart:
“You just bummed the @*!# out of me.”
wildbluffmedia.com
From the Daily Show (September 18, 2007; @5:13)
The full inteview is here.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.7 of 155
Economics, Schmeconomics
Greenspan continues:“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years.
I’m no better than I ever was,and nobody else is. Forecasting 50 years ago was as good or asbad as it is today. And the reason is that human nature hasn’tchanged. We can’t improve ourselves.”
Jon Stewart:
“You just bummed the @*!# out of me.”
wildbluffmedia.com
From the Daily Show (September 18, 2007; @5:13)
The full inteview is here.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.7 of 155
Economics, Schmeconomics
Greenspan continues:“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years. I’m no better than I ever was,
and nobody else is. Forecasting 50 years ago was as good or asbad as it is today. And the reason is that human nature hasn’tchanged. We can’t improve ourselves.”
Jon Stewart:
“You just bummed the @*!# out of me.”
wildbluffmedia.com
From the Daily Show (September 18, 2007; @5:13)
The full inteview is here.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.7 of 155
Economics, Schmeconomics
Greenspan continues:“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years. I’m no better than I ever was,and nobody else is.
Forecasting 50 years ago was as good or asbad as it is today. And the reason is that human nature hasn’tchanged. We can’t improve ourselves.”
Jon Stewart:
“You just bummed the @*!# out of me.”
wildbluffmedia.com
From the Daily Show (September 18, 2007; @5:13)
The full inteview is here.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.7 of 155
Economics, Schmeconomics
Greenspan continues:“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years. I’m no better than I ever was,and nobody else is. Forecasting 50 years ago was as good or asbad as it is today.
And the reason is that human nature hasn’tchanged. We can’t improve ourselves.”
Jon Stewart:
“You just bummed the @*!# out of me.”
wildbluffmedia.com
From the Daily Show (September 18, 2007; @5:13)
The full inteview is here.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.7 of 155
Economics, Schmeconomics
Greenspan continues:“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years. I’m no better than I ever was,and nobody else is. Forecasting 50 years ago was as good or asbad as it is today. And the reason is that human nature hasn’tchanged.
We can’t improve ourselves.”
Jon Stewart:
“You just bummed the @*!# out of me.”
wildbluffmedia.com
From the Daily Show (September 18, 2007; @5:13)
The full inteview is here.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.7 of 155
Economics, Schmeconomics
Greenspan continues:“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years. I’m no better than I ever was,and nobody else is. Forecasting 50 years ago was as good or asbad as it is today. And the reason is that human nature hasn’tchanged. We can’t improve ourselves.”
Jon Stewart:
“You just bummed the @*!# out of me.”
wildbluffmedia.com
From the Daily Show (September 18, 2007; @5:13)
The full inteview is here.
COcoNuTS@networksvox
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.7 of 155
Economics, Schmeconomics
Greenspan continues:“The trouble is that we can’t figure that out. I’ve been in theforecasting business for 50 years. I’m no better than I ever was,and nobody else is. Forecasting 50 years ago was as good or asbad as it is today. And the reason is that human nature hasn’tchanged. We can’t improve ourselves.”
Jon Stewart:
“You just bummed the @*!# out of me.”
wildbluffmedia.com
From the Daily Show (September 18, 2007; @5:13)
The full inteview is here.
COcoNuTS@networksvox
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.9 of 155
Robert Kennedy on the Gross Domestic Product(GDP) in 1968:“It measures everything except that which makes lifeworthwhile.
And it can tell us everything aboutAmerica except why we are proud that we areAmericans.” [21, 20]
COcoNuTS@networksvox
Happiness
Introduction
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Hedonometer
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.9 of 155
Robert Kennedy on the Gross Domestic Product(GDP) in 1968:“It measures everything except that which makes lifeworthwhile. And it can tell us everything aboutAmerica except why we are proud that we areAmericans.” [21, 20]
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
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.10 of 155
Basic Science ≃ Describe + Explain:
Lord Kelvin (possibly):
“To measure is to know.”
“If you cannot measure it, youcannot improve it.”
But also:
“There is nothing new to bediscovered in physics now. Allthat remains is more and moreprecise measurement.”
“X-rays will prove to be a hoax.”
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
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.10 of 155
Basic Science ≃ Describe + Explain:
Lord Kelvin (possibly):
“To measure is to know.”
“If you cannot measure it, youcannot improve it.”
But also:
“There is nothing new to bediscovered in physics now. Allthat remains is more and moreprecise measurement.”
“X-rays will prove to be a hoax.”
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
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Other Emotions
References
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.10 of 155
Basic Science ≃ Describe + Explain:
Lord Kelvin (possibly):
“To measure is to know.”
“If you cannot measure it, youcannot improve it.”
But also:
“There is nothing new to bediscovered in physics now. Allthat remains is more and moreprecise measurement.”
“X-rays will prove to be a hoax.”
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.10 of 155
Basic Science ≃ Describe + Explain:
Lord Kelvin (possibly):
“To measure is to know.”
“If you cannot measure it, youcannot improve it.”
But also: “There is nothing new to be
discovered in physics now. Allthat remains is more and moreprecise measurement.”
“X-rays will prove to be a hoax.”
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.10 of 155
Basic Science ≃ Describe + Explain:
Lord Kelvin (possibly):
“To measure is to know.”
“If you cannot measure it, youcannot improve it.”
But also: “There is nothing new to be
discovered in physics now. Allthat remains is more and moreprecise measurement.”
“X-rays will prove to be a hoax.”
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
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.11 of 155
A brief history of measuring time: Megaliths for Big Time Sundials, 1500 BC, Egypt (solid for over 2000 years) Escapements (200s), Hourglasses (1300s?),
Pendulum clocks (Galileo, 1500s) Chronometers, 1700s:
“Longitude: The True Story of a Lone GeniusWho Solved the Greatest Scientific Problemof His Time , publisher=BloomsburyPublishing, US”by Dava Sobel (2007). [33]
Billionths of a second accuracy: Atomic clocks(Lord Kelvin, 1879)
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
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Hedonometer
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.12 of 155
Measuring temperature was thought impossible:
From “Bias in Mental Testing”, Arthur Jensen, 1980 [17]
per @SilverVVulpes: Also: Inventing Temperature, HasokChang, 2004 [3]
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
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Hedonometer
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.13 of 155
Measuring temperature was thought impossible:
From “Bias in Mental Testing”, Arthur Jensen, 1980 [17]
per @SilverVVulpes: Also: Inventing Temperature, HasokChang, 2004 [3]
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
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References
.....
.14 of 155
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
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Hedonometer
Analysis
Songs
Blogs
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.14 of 155
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
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References
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.14 of 155
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
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Hedonometer
Analysis
Songs
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.14 of 155
COcoNuTS@networksvox
Happiness
Introduction
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HappinessSome motivation
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Hedonometer
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.15 of 155
What matters and what’s measurable:
COcoNuTS@networksvox
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Introduction
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.16 of 155
Outline
Introduction
Measurement
HappinessSome motivationMeasuring emotional contentHedonometerAnalysisSongsBlogsSOTUGeographyMovementOther Emotions
References
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
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.17 of 155
Panometer—Three kinds of lexical meters:
1. Principled lexical meters: The Hedonometer.
The Lexicocalorimeter.
2. Ground truth lexical meters: Insomniometer. Hangoverometer.
3. Bootstrap lexical meters: Boredometer. Hashtagometers.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
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Other Emotions
References
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.17 of 155
Panometer—Three kinds of lexical meters:
1. Principled lexical meters: The Hedonometer. The Lexicocalorimeter.
2. Ground truth lexical meters: Insomniometer. Hangoverometer.
3. Bootstrap lexical meters: Boredometer. Hashtagometers.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.17 of 155
Panometer—Three kinds of lexical meters:
1. Principled lexical meters: The Hedonometer. The Lexicocalorimeter.
2. Ground truth lexical meters: Insomniometer. Hangoverometer.
3. Bootstrap lexical meters: Boredometer. Hashtagometers.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.17 of 155
Panometer—Three kinds of lexical meters:
1. Principled lexical meters: The Hedonometer. The Lexicocalorimeter.
2. Ground truth lexical meters: Insomniometer. Hangoverometer.
3. Bootstrap lexical meters: Boredometer. Hashtagometers.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
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.18 of 155
Measuring Happiness:
Socrates et al.:eudaimonia [18]
Bentham:hedonisticcalculus
Jefferson:…the pursuit ofhappiness
COcoNuTS@networksvox
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Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
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.19 of 155
Early drafts:
COcoNuTS@networksvox
Happiness
Introduction
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HappinessSome motivation
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.20 of 155
Happiness:
Even the odd modern economistis happy:
“Happiness” by RichardLayard [24]
[amazon]
COcoNuTS@networksvox
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Introduction
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.21 of 155
What makes us happy?—Layard’ssummary:Dominant factors:
Familyrelationships
Financialsituation
Work Community and
Friends
Health Personal Values Personal
Freedom
Unimportant factors:
Age Gender Education
Inherentintelligence
Looks
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
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.21 of 155
What makes us happy?—Layard’ssummary:Dominant factors:
Familyrelationships
Financialsituation
Work Community and
Friends
Health Personal Values Personal
Freedom
Unimportant factors:
Age Gender Education
Inherentintelligence
Looks
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.22 of 155
Desiring happiness—not just for boffins: Average people routinely report being happy is
what they want most in life [24, 25, 8]
And it matters: “Happy people live longer:…”Survey by Diener and Chan. [8]
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
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.22 of 155
Desiring happiness—not just for boffins: Average people routinely report being happy is
what they want most in life [24, 25, 8]
And it matters: “Happy people live longer:…”Survey by Diener and Chan. [8]
National indices ofwell-being: Bhutan UK France Australia
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
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.23 of 155
Outline
Introduction
Measurement
HappinessSome motivationMeasuring emotional contentHedonometerAnalysisSongsBlogsSOTUGeographyMovementOther Emotions
References
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
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Hedonometer
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.24 of 155
Some easy knocks:
“The Passionate State of Mind: And OtherAphorisms”by Eric Hoffer (1954). [16]
“The search for happiness is one of the chief sourcesof unhappiness.”
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
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.25 of 155
Some easy knocks:
Colbert: “Happiness is totally overrated …”
“Happiness is for the weak.”
Full interview with Jennifer Senior here (2014/02/03)
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
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.25 of 155
Some easy knocks:
Colbert: “Happiness is totally overrated …”“Happiness is for the weak.”
Full interview with Jennifer Senior here (2014/02/03)
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
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.26 of 155
30 Rock, S7E8:
COcoNuTS@networksvox
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.27 of 155
30 Rock, S7E8:
JD: “Before she died, Colleen told me—she just wants me to behappy.
‘I just want you to be happy.’ You know who you say thatto? A loser. Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
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.27 of 155
30 Rock, S7E8:
JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’
You know who you say thatto? A loser. Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
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References
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.27 of 155
30 Rock, S7E8:
JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’ You know who you say thatto?
A loser. Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
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References
.....
.27 of 155
30 Rock, S7E8:
JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’ You know who you say thatto? A loser.
Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
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.27 of 155
30 Rock, S7E8:
JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’ You know who you say thatto? A loser. Someone who can’t hope for anything more in lifethan just being happy.
You say that to someone who hasdisappointed you.”LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
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.27 of 155
30 Rock, S7E8:
JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’ You know who you say thatto? A loser. Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”
LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
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.27 of 155
30 Rock, S7E8:
JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’ You know who you say thatto? A loser. Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”LL: “Jack.”
JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
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References
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.27 of 155
30 Rock, S7E8:
JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’ You know who you say thatto? A loser. Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service.
One more chanceto disappoint her as she looks up at me from her throne In hell.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
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.27 of 155
30 Rock, S7E8:
JD: “Before she died, Colleen told me—she just wants me to behappy. ‘I just want you to be happy.’ You know who you say thatto? A loser. Someone who can’t hope for anything more in lifethan just being happy. You say that to someone who hasdisappointed you.”LL: “Jack.”JD: “No. It’s perfect. She’s a genius. One last twist of the knife. Well,thank you for coming, Lemon, but I better get going. The funeral istomorrow. Colleen wanted to be buried before the rest of thefamily found out and sold her body to a haunted house. And, ofcourse, I get to eulogize Colleen at the service. One more chanceto disappoint her as she looks up at me from her throne In hell.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
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.28 of 155
Meaning rather than happiness:
“Mindfulness in Plain English”by Henepola Gunaratana (1992). [15]
“Flow”by Mihaly Csikszentmihalyi (1990). [6]
Can we measure Flow in a big data way? Maybe drops in social media usage indicate
people are doing okay?
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
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.29 of 155
Emotional content
So how does one measure1. happiness?2. levels of other emotional states?
Just ask people how happy they are.
Experience sampling [5, 7, 6] (Csikszentmihalyi et al.) Day reconstruction [19] (Kahneman et al.)
But self-reporting has some drawbacks: relies on memory and self-perception induces misreporting [26]
costly
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
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.29 of 155
Emotional content
So how does one measure1. happiness?2. levels of other emotional states?
Just ask people how happy they are.
Experience sampling [5, 7, 6] (Csikszentmihalyi et al.) Day reconstruction [19] (Kahneman et al.)
But self-reporting has some drawbacks: relies on memory and self-perception induces misreporting [26]
costly
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
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References
.....
.29 of 155
Emotional content
So how does one measure1. happiness?2. levels of other emotional states?
Just ask people how happy they are. Experience sampling [5, 7, 6] (Csikszentmihalyi et al.) Day reconstruction [19] (Kahneman et al.)
But self-reporting has some drawbacks: relies on memory and self-perception induces misreporting [26]
costly
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.29 of 155
Emotional content
So how does one measure1. happiness?2. levels of other emotional states?
Just ask people how happy they are. Experience sampling [5, 7, 6] (Csikszentmihalyi et al.) Day reconstruction [19] (Kahneman et al.)
But self-reporting has some drawbacks: relies on memory and self-perception induces misreporting [26]
costly
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
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.....
.30 of 155
Happiness, attention, and doing:
Fig. 1. Mean happiness reported during each ac-tivity (top) and while mind wandering to unpleas-ant topics, neutral topics, pleasant topics or notmind wandering (bottom). Dashed line indicatesmean of happiness across all samples. Bubble areaindicates the frequency of occurrence. The largestbubble (“not mind wandering”) corresponds to53.1% of the samples, and the smallest bubble(“praying/worshipping/meditating”) corresponds to0.1% of the samples.
Killingsworth andGilbert, Science,2010 [22]
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
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Outline
Introduction
Measurement
HappinessSome motivationMeasuring emotional contentHedonometerAnalysisSongsBlogsSOTUGeographyMovementOther Emotions
References
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
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We’d like to build an ‘hedonometer’:
An instrument to ‘remotely-sense’emotional states and levels, inreal time or post hoc.
Ideally:
Transparent Fast Based on written
expression Uses human
evaluation
Non-reactive Complementary to
self-reportedmeasures
Improvable
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
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We don’t want to end up here:
Science Policy
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
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.34 of 155
Or here: Facebook Lexicon SentimentAnalysis (2008)
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
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Facebook Lexicon Sentiment Analysis
Binary decision on emotional content
Limitation: Sentiments are classified as eitherpositive or negative.
‘I like Sarah Palin’ given same score as ‘SarahPalin’s voice fills me with unbridled joy!’
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.35 of 155
Facebook Lexicon Sentiment Analysis
Binary decision on emotional content
Limitation: Sentiments are classified as eitherpositive or negative.
‘I like Sarah Palin’ given same score as ‘SarahPalin’s voice fills me with unbridled joy!’
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.35 of 155
Facebook Lexicon Sentiment Analysis
Binary decision on emotional content
Limitation: Sentiments are classified as eitherpositive or negative.
‘I like Sarah Palin’ given same score as ‘SarahPalin’s voice fills me with unbridled joy!’
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.36 of 155
Measuring Emotional Content Idea: Build on measures of the emotional content
of individual words.
Osgood et al. (1957) [30] identifieda basis of three psychological variables assemantic differentials:
Valence: bad ↔ good Arousal: passive ↔ active Dominance: weak ↔ strong Also often: Evaluation, Activity, and Potency.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.36 of 155
Measuring Emotional Content Idea: Build on measures of the emotional content
of individual words. Osgood et al. (1957) [30] identified
a basis of three psychological variables assemantic differentials:
Valence: bad ↔ good Arousal: passive ↔ active Dominance: weak ↔ strong Also often: Evaluation, Activity, and Potency.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.36 of 155
Measuring Emotional Content Idea: Build on measures of the emotional content
of individual words. Osgood et al. (1957) [30] identified
a basis of three psychological variables assemantic differentials: Valence: bad ↔ good Arousal: passive ↔ active Dominance: weak ↔ strong Also often: Evaluation, Activity, and Potency.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.37 of 155
ANEW study
ANEW = “Affective Norms for English Words”
Study: participants shown lists of isolated words Asked to grade each word’s valence, arousal, and
dominance level Integer scale of 1–9
𝑁 =1034 words—previously identified as bearingemotional weight
Participants = College students (*cough*) Results published by Bradley and Lang (1999) [2]
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.38 of 155
1999 ANEW study—three 1–9 scales: [2]valence:
arousal:
dominance:
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.39 of 155
ANEW study:
Valence = Happiness: Valence scale presented to participants as a
‘happy-unhappy scale.’
Participants were further told:
“At one extreme of this scale, you are happy,pleased, satisfied, contented, hopeful. …
The other end of the scale is when you feelcompletely unhappy, annoyed, unsatisfied,melancholic, despaired, or bored.”
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.39 of 155
ANEW study:
Valence = Happiness: Valence scale presented to participants as a
‘happy-unhappy scale.’ Participants were further told:
“At one extreme of this scale, you are happy,pleased, satisfied, contented, hopeful. …
The other end of the scale is when you feelcompletely unhappy, annoyed, unsatisfied,melancholic, despaired, or bored.”
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.39 of 155
ANEW study:
Valence = Happiness: Valence scale presented to participants as a
‘happy-unhappy scale.’ Participants were further told:
“At one extreme of this scale, you are happy,pleased, satisfied, contented, hopeful. …
The other end of the scale is when you feelcompletely unhappy, annoyed, unsatisfied,melancholic, despaired, or bored.”
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
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.40 of 155
ANEW study words—examples
0 50 1001502001
2
3
4
5
6
7
8
9
funeral/rape/suicide
trauma/hostage/disgusted
fault/corrupt/lawsuit
derelict/neurotic/vanity
engine/paper/street
optimism/pancakes/church
glory/luxury/trophy
love/paradise/triumphant
frequency
vale
nce v
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
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Analysing text: Simplest measure for a text:𝜃avg = 𝑁∑𝑖=1 𝑝𝑖𝜃𝑖
where 𝑝𝑖 is fractional abundance of word 𝑖 and 𝜃 isaverage valence, arousal, or dominance for word 𝑖.
Focus on happiness (valence), 𝜃 = ℎ. Average happiness typically falls between 5 and 7.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.41 of 155
Analysing text: Simplest measure for a text:𝜃avg = 𝑁∑𝑖=1 𝑝𝑖𝜃𝑖
where 𝑝𝑖 is fractional abundance of word 𝑖 and 𝜃 isaverage valence, arousal, or dominance for word 𝑖.
Focus on happiness (valence), 𝜃 = ℎ.
Average happiness typically falls between 5 and 7.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.41 of 155
Analysing text: Simplest measure for a text:𝜃avg = 𝑁∑𝑖=1 𝑝𝑖𝜃𝑖
where 𝑝𝑖 is fractional abundance of word 𝑖 and 𝜃 isaverage valence, arousal, or dominance for word 𝑖.
Focus on happiness (valence), 𝜃 = ℎ. Average happiness typically falls between 5 and 7.
Measuring the perceived happiness of atext:
ANEWwords
11. perfume
14. lie
k=1. love2. mother3. baby4. beauty5. truth6. people7. strong8. young9. girl10. movie
12. queen13. name
8.728.398.227.827.807.337.116.896.876.866.766.445.552.79
11311
12411111
from a movie scene.
’cause the lie becomes the truth.
And be careful of what you do
She’s just a girl who claims
Billie Jean is not my lover,
that I am the one.
Michael Jackson’s Billie Jean
vMichaelJackson
vThriller
= 7.1
= 6.4
= 6.3
=vtext ∑
kfk
vBillie Jean
∑
kvkfk
fk
“She was more like a beauty queen
2And mother always told me,
be careful who you love.
vkLyrics for
Temperature-like measure—large numbers only.
Not meant to be used at level of sentence,paragraph, song, tweet, ...
Important: Social measure of sentiment. Later: see instrument is tunable.
Measuring the perceived happiness of atext:
ANEWwords
11. perfume
14. lie
k=1. love2. mother3. baby4. beauty5. truth6. people7. strong8. young9. girl10. movie
12. queen13. name
8.728.398.227.827.807.337.116.896.876.866.766.445.552.79
11311
12411111
from a movie scene.
’cause the lie becomes the truth.
And be careful of what you do
She’s just a girl who claims
Billie Jean is not my lover,
that I am the one.
Michael Jackson’s Billie Jean
vMichaelJackson
vThriller
= 7.1
= 6.4
= 6.3
=vtext ∑
kfk
vBillie Jean
∑
kvkfk
fk
“She was more like a beauty queen
2And mother always told me,
be careful who you love.
vkLyrics for
Temperature-like measure—large numbers only. Not meant to be used at level of sentence,
paragraph, song, tweet, ...
Important: Social measure of sentiment. Later: see instrument is tunable.
Measuring the perceived happiness of atext:
ANEWwords
11. perfume
14. lie
k=1. love2. mother3. baby4. beauty5. truth6. people7. strong8. young9. girl10. movie
12. queen13. name
8.728.398.227.827.807.337.116.896.876.866.766.445.552.79
11311
12411111
from a movie scene.
’cause the lie becomes the truth.
And be careful of what you do
She’s just a girl who claims
Billie Jean is not my lover,
that I am the one.
Michael Jackson’s Billie Jean
vMichaelJackson
vThriller
= 7.1
= 6.4
= 6.3
=vtext ∑
kfk
vBillie Jean
∑
kvkfk
fk
“She was more like a beauty queen
2And mother always told me,
be careful who you love.
vkLyrics for
Temperature-like measure—large numbers only. Not meant to be used at level of sentence,
paragraph, song, tweet, ... Important: Social measure of sentiment.
Later: see instrument is tunable.
Measuring the perceived happiness of atext:
ANEWwords
11. perfume
14. lie
k=1. love2. mother3. baby4. beauty5. truth6. people7. strong8. young9. girl10. movie
12. queen13. name
8.728.398.227.827.807.337.116.896.876.866.766.445.552.79
11311
12411111
from a movie scene.
’cause the lie becomes the truth.
And be careful of what you do
She’s just a girl who claims
Billie Jean is not my lover,
that I am the one.
Michael Jackson’s Billie Jean
vMichaelJackson
vThriller
= 7.1
= 6.4
= 6.3
=vtext ∑
kfk
vBillie Jean
∑
kvkfk
fk
“She was more like a beauty queen
2And mother always told me,
be careful who you love.
vkLyrics for
Temperature-like measure—large numbers only. Not meant to be used at level of sentence,
paragraph, song, tweet, ... Important: Social measure of sentiment. Later: see instrument is tunable.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.43 of 155
Outline
Introduction
Measurement
HappinessSome motivationMeasuring emotional contentHedonometerAnalysisSongsBlogsSOTUGeographyMovementOther Emotions
References
Daft Punk’s “Around the World”:
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world.
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Around the world, around the worldAround the world, around the worldAround the world, around the worldAround the world, around the world
Magic: Low entropy, high energy.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.45 of 155
Outline
Introduction
Measurement
HappinessSome motivationMeasuring emotional contentHedonometerAnalysisSongsBlogsSOTUGeographyMovementOther Emotions
References
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.46 of 155
Song Lyrics—average happiness
1960 1970 1980 1990 2000 20105.9
6
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
year
mea
n va
lenc
e v av
g
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.47 of 155
Song Lyrics—measurement robustness
19601965197019751980198519901995200020052010−0.4
−0.2
0
0.2
0.4
0.6
0.8
rel.
val.
(v −
v avg)
year
5.8 6 6.2 6.4 6.60
100
200
vavg
coun
t
100 random subsets of 750 ANEW words
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.48 of 155
Song Lyrics—average happiness of genres:
1960 1970 1980 1990 2000 20104.5
5
5.5
6
6.5
7
year
mea
n va
lenc
e v av
g
Gospel/Soul (6.91)Pop (6.69)Reggae (6.40)Rock (6.27)Rap/Hip−Hop (6.01)Punk (5.61)Metal/Industrial (5.10)
Happiness Word Shift Graph (early version):
−20 −10 0 10
25242322212019181716151413121110987654321 love ↓
lonely ↓hate ↑pain ↑baby ↓death ↑dead ↑home ↓sick ↑fear ↑hit ↑hell ↑fall ↑sin ↑lost ↑
sad ↓burn ↑lie ↑scared ↑afraid ↑music ↓
life ↑god ↑
trouble ↓loneliness ↓
Per word valence shift ∆i
Wor
d nu
mbe
r i
Per word drop in valence of lyrics from 1980−2007 relative to valence of lyrics from 1960−1979:
lonely ↓sad ↓
trouble ↓loneliness ↓
devil ↓Decreases in relativelylow valence wordscontribute to increasein average valence
life ↑god ↑
truth ↑party ↑
sex ↑Increases in relativelyhigh valence wordscontribute to increasein average valence
hate ↑pain ↑death ↑dead ↑sick ↑
Increases in relativelylow valence wordscontribute to dropin average valence
love ↓baby ↓home ↓music ↓good ↓
Decreases in relativelyhigh valence wordscontribute to dropin average valence
Key:
Word shifts are word clouds for grown ups.
Word data shift details:
Given two texts 𝑇ref and 𝑇comp:
Measure difference in average happiness:ℎ(comp)avg − ℎ(ref)
avg
Evident question: Which words contribute themost to this change?
Break difference down by contributions fromindividual words:𝛿ℎavg,i = 100∣ℎ(comp)
avg − ℎ(ref)avg ∣ [ℎavg(𝑤𝑖) − ℎ(ref)
avg ]⏟⏟⏟⏟⏟⏟⏟+/− [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]⏟⏟⏟⏟⏟⏟⏟↑/↓ Must have: ∑𝑖 𝛿ℎavg,i = ±100 Rank words by |𝛿ℎavg,i|
Word data shift details:
Given two texts 𝑇ref and 𝑇comp:
Measure difference in average happiness:ℎ(comp)avg − ℎ(ref)
avg
Evident question: Which words contribute themost to this change?
Break difference down by contributions fromindividual words:𝛿ℎavg,i = 100∣ℎ(comp)
avg − ℎ(ref)avg ∣ [ℎavg(𝑤𝑖) − ℎ(ref)
avg ]⏟⏟⏟⏟⏟⏟⏟+/− [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]⏟⏟⏟⏟⏟⏟⏟↑/↓ Must have: ∑𝑖 𝛿ℎavg,i = ±100 Rank words by |𝛿ℎavg,i|
Word data shift details:
Given two texts 𝑇ref and 𝑇comp:
Measure difference in average happiness:ℎ(comp)avg − ℎ(ref)
avg
Evident question: Which words contribute themost to this change?
Break difference down by contributions fromindividual words:𝛿ℎavg,i = 100∣ℎ(comp)
avg − ℎ(ref)avg ∣ [ℎavg(𝑤𝑖) − ℎ(ref)
avg ]⏟⏟⏟⏟⏟⏟⏟+/− [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]⏟⏟⏟⏟⏟⏟⏟↑/↓
Must have: ∑𝑖 𝛿ℎavg,i = ±100 Rank words by |𝛿ℎavg,i|
Word data shift details:
Given two texts 𝑇ref and 𝑇comp:
Measure difference in average happiness:ℎ(comp)avg − ℎ(ref)
avg
Evident question: Which words contribute themost to this change?
Break difference down by contributions fromindividual words:𝛿ℎavg,i = 100∣ℎ(comp)
avg − ℎ(ref)avg ∣ [ℎavg(𝑤𝑖) − ℎ(ref)
avg ]⏟⏟⏟⏟⏟⏟⏟+/− [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]⏟⏟⏟⏟⏟⏟⏟↑/↓ Must have: ∑𝑖 𝛿ℎavg,i = ±100
Rank words by |𝛿ℎavg,i|
Word data shift details:
Given two texts 𝑇ref and 𝑇comp:
Measure difference in average happiness:ℎ(comp)avg − ℎ(ref)
avg
Evident question: Which words contribute themost to this change?
Break difference down by contributions fromindividual words:𝛿ℎavg,i = 100∣ℎ(comp)
avg − ℎ(ref)avg ∣ [ℎavg(𝑤𝑖) − ℎ(ref)
avg ]⏟⏟⏟⏟⏟⏟⏟+/− [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]⏟⏟⏟⏟⏟⏟⏟↑/↓ Must have: ∑𝑖 𝛿ℎavg,i = ±100 Rank words by |𝛿ℎavg,i|
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.51 of 155
Word data shift details:
ℎ(comp)avg − ℎ(ref)
avg = 𝑁∑𝑖=1 ℎavg(𝑤𝑖)𝑝(comp)𝑖 − 𝑁∑𝑖=1 ℎavg(𝑤𝑖)𝑝(ref)𝑖= 𝑁∑𝑖=1 ℎavg(𝑤𝑖) [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]
= 𝑁∑𝑖=1 [ℎavg(𝑤𝑖) − ℎ(ref)avg ] [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]
where𝑁∑𝑖=1 ℎ(ref)avg [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ] = ℎ(ref)
avg
𝑁∑𝑖=1 [𝑝(comp)𝑖 − 𝑝(ref)𝑖 ]= ℎ(ref)
avg (1 − 1) = 0.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
.52 of 155
+↑: Increased usage of relatively positive words—If a wordis happier than text 𝑇ref (+) and appears relativelymore often in text 𝑇comp (↑), then the contribution tothe difference ℎ(comp)
avg − ℎ(ref)avg is positive;−↓: Decreased usage of relatively negative words—If a
word is less happy than text 𝑇ref (−) and appearsrelatively less often in text 𝑇comp (↓), then thecontribution to the difference ℎ(comp)
avg − ℎ(ref)avg is also
positive;+↓: Decreased usage of relatively positive words—If a wordis happier than text 𝑇ref (+) and appears relatively lessoften in text 𝑇comp (↓), then the contribution to thedifference ℎ(comp)
avg − ℎ(ref)avg is negative; and−↑: Increased usage of relatively negative words—If a word
is less happy than text 𝑇ref (−) and appears relativelymore often in text 𝑇comp (↑), then the contribution tothe difference ℎ(comp)
avg − ℎ(ref)avg is also negative.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
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Top 50 of ≃ 20,000 artists:Rank Artist ℎavg1 All-4-One 7.152 Luther Vandross 7.123 S Club 7 7.054 K Ci & JoJo 7.045 Perry Como 7.046 Diana Ross & The Supremes 7.037 Buddy Holly 7.028 Faith Evans 7.019 The Beach Boys 7.0110 Jon B 6.9811 Dru Hill 6.9612 Earth Wind & Fire 6.9513 Ashanti 6.9514 Otis Redding 6.9315 Faith Hill 6.9316 NSync 6.9317 The Supremes 6.9118 The Partridge Family 6.9119 Kelly Price 6.8920 Tamia 6.8921 Avant 6.8822 Jennifer Lopez 6.8823 Vanessa Williams 6.8724 Babyface 6.8725 E Rotic 6.87
Rank Artist ℎavg26 Sarah Connor 6.8627 Darlene Zschech 6.8628 Mary J Blige 6.8629 Steve Miller Band 6.8630 New Edition 6.8631 Mandy Moore 6.8632 Alicia Keys 6.8533 Cher 6.8534 Modern Talking 6.8535 Mario 6.8436 Aretha Franklin 6.8437 Jessica Simpson 6.8438 112 6.8439 Backstreet Boys 6.8340 Billy Gilman 6.8341 B2K 6.8242 Stevie Wonder 6.8243 John Legend 6.8144 Ricky Nelson 6.7945 Lionel Richie 6.7946 98 Degrees 6.7947 Boyzone 6.7948 Gerald Levert 6.7949 Nat King Cole 6.7850 Marques Houston 6.78
(criteria: ≥ 50 songs and ≥ 1000 ANEW words)
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Bottom 50 of ≃ 20,000 artists:Rank Artist ℎavg1 Slayer 4.802 Misfits 4.883 Staind 4.934 Slipknot 4.985 Darkthrone 4.986 Death 5.027 Black Label Society 5.058 Pig 5.089 Voivod 5.1410 Fear Factory 5.1511 Iced Earth 5.1612 Simple Plan 5.1613 Machine Head 5.1714 Metallica 5.1915 Dimmu Borgir 5.2016 Mudvayne 5.2117 Linkin Park 5.2218 Papa Roach 5.2219 Audioslave 5.2420 Rage Against The Machine 5.2421 Cradle Of Filth 5.2522 Dark Tranquility 5.2623 Jack Off Jill 5.2824 Evanescence 5.3025 Twiztid 5.33
Rank Artist ℎavg26 Nine Inch Nails 5.3427 Sevendust 5.3428 Annihilator 5.3529 Biohazard 5.3630 Insane Clown Posse 5.3631 Megadeth 5.3632 Manowar 5.3733 Zebrahead 5.3834 Danzig 5.3935 Acid Drinkers 5.4036 Dag Nasty 5.4037 Iron Maiden 5.4038 Flotsam And Jetsam 5.4139 Powerman 5000 5.4240 Anthrax 5.4341 Rhapsody 5.4342 Korn 5.4343 Rage 5.4444 Accept 5.4545 Esham 5.4646 Blind Guardian 5.4647 White Zombie 5.4748 Helloween 5.5049 W A S P 5.5050 Green Day 5.50
(criteria: ≥ 50 songs and ≥ 1000 ANEW words)
Text: ℎavg Words with a similar score:
Soul/Gospellyrics [10]
6.9 chocolate (6.88), leisurely (6.88), penthouse(6.81)
Pop lyrics [10] 6.7 dream (6.73), honey (6.73), sugar (6.74)Dante’s Paradise [?] 6.5 muffin (6.57), rabbit (6.57), smooth (6.58)Tweets, 9/9/2008 to12/31/2010
6.4 thought (6.39), face (6.39), blond (6.42)
Rock lyrics [10] 6.3 church (6.28), tree (6.32), air (6.34)Enron Emails [?] 6.2 clouds (6.18), alert (6.20), computer (6.24)State of the UnionMessages [10]
6.1 grass (6.12), idol (6.12), bottle (6.15)
New York Times(1987–2007) [32]
6.0 hotel (6.00), tennis (6.02), wonder (6.03)
Blogs [10] 5.8 owl (5.80), whistle (5.81), humble (5.86)Dante’s Inferno [?] 5.5 glacier (5.50), repentant (5.53), mischief (5.57)Heavy Metallyrics [10]
5.4 lamp (5.41), elevator (5.44), truck (5.47)
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Lexicon Valley, Episode #62, June 17, 2015 Mike Vuolo and Bob Garfield.
Language has a Positivity Bias. How did we measure that?
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Outline
Introduction
Measurement
HappinessSome motivationMeasuring emotional contentHedonometerAnalysisSongsBlogsSOTUGeographyMovementOther Emotions
References
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Data sets: Blog phrases containing “I feel...”, “I am feeling”,
etc., taken from wefeelfine.org (API, 2005–2010)
Created byJonathan Harris& Sep Kamvar
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Jonathan Harris:
2007 TED Talk: The Web’s secret stories
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So I really consider myself a storyteller. But I don’treally tell stories in the usual way, in the sense that Idon’t usually tell my own stories. Instead, I’m reallyinterested in building tools that allow large numbers ofother people to tell their stories, people all around theworld. I do this because I think that people actuallyhave a lot in common. I think people are very similar,but I also think that we have trouble seeing that.
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wefeelfine.org:
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wefeelfine.org:
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wefeelfine.org:
Blogs—Overall trend
A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A5.7
5.8
5.9
6
6.1
6.2
6.3
6.4
9/119/10
9/109/10
US Election 11/4
US Inauguration 1/20
MichaelJackson
♥
♥
♥ ♥♥
2005 2006 2007 2008 2009 2010
aver
age
happ
ines
s h avg
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From wefeelfine.org by Jonathan Harris & SepKamvar
13 20 30 40 50 60 70 805.5
5.6
5.7
5.8
5.9
6
6.1
blogger age
vale
nce
(v)
Average happiness as a function of the agebloggers report they will turn in the year of theirposting.
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Blogs—Age
Self-report studies find little variation in happinesswith age [12, 13]
Surprising: Expect a rise and fall. A ‘challenge’ for theory... Related to the Easterlin Paradox:
Money doesn’t buy happiness But maybe it does a little bit—Veenhoven &
Hagerty (2003) and Wolfers & Stevenson (2008).
COcoNuTS@networksvox
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Blogs—Age
Self-report studies find little variation in happinesswith age [12, 13]
Surprising: Expect a rise and fall.
A ‘challenge’ for theory... Related to the Easterlin Paradox:
Money doesn’t buy happiness But maybe it does a little bit—Veenhoven &
Hagerty (2003) and Wolfers & Stevenson (2008).
COcoNuTS@networksvox
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Blogs—Age
Self-report studies find little variation in happinesswith age [12, 13]
Surprising: Expect a rise and fall. A ‘challenge’ for theory...
Related to the Easterlin Paradox:Money doesn’t buy happiness
But maybe it does a little bit—Veenhoven &Hagerty (2003) and Wolfers & Stevenson (2008).
COcoNuTS@networksvox
Happiness
Introduction
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Blogs—Age
Self-report studies find little variation in happinesswith age [12, 13]
Surprising: Expect a rise and fall. A ‘challenge’ for theory... Related to the Easterlin Paradox:
Money doesn’t buy happiness
But maybe it does a little bit—Veenhoven &Hagerty (2003) and Wolfers & Stevenson (2008).
COcoNuTS@networksvox
Happiness
Introduction
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Blogs—Age
Self-report studies find little variation in happinesswith age [12, 13]
Surprising: Expect a rise and fall. A ‘challenge’ for theory... Related to the Easterlin Paradox:
Money doesn’t buy happiness But maybe it does a little bit—Veenhoven &
Hagerty (2003) and Wolfers & Stevenson (2008).
COcoNuTS@networksvox
Happiness
Introduction
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−20 0 20
1
5
10
15
20
25
−↑sick
−↑hate
−↑stupid
−↑sad
happy +↑
love +↑
−↑depressed
−↑bored
−↑lonely
−↑alone
−↑mad
−↑pain
+↓life
loved +↑
−↑upset
−↑fat
fun +↑
−↑dead
−↑scared
−↑terrible
friend +↑
people +↑
−↑confused
time −↓
−↑hurt
Per word average happiness shift δhavg,r (%)
Word
rank
r
Tre f: born in 1960-1969 (havg=5.96)Tcom p: 14 year s old (havg=5.55)
Text s iz e :T re f T c omp
+↓ +↑
−↑ −↓
Balan ce :
−169 : +69
−100 0
100
101
102
103
∑r
i =1δ havg, i
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Blogs—Latitude
0 10 20 30 40 50 60 70
5.7
5.75
5.8
5.85
latitude |degrees|
Near equator—social factors Increase in ‘sad’, ‘bored’,
‘lonely’, ‘stupid’, ‘guilty’ Decrease in ‘good’ and
‘people’
Near poles—social/psychological/climate Increase in ‘sick’, ‘guilty’,
‘cold’, ‘depressed’, and‘headache’ and decrease of‘love’ and ‘life.’
Offset by decrease in ‘hurt’and ‘pain.’
More ‘bed’ and ‘sleep.’
COcoNuTS@networksvox
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−100 −50 0 50 100
1
5
10
15
20
25
love +↑
−↑hurt
−↑hate
−↑sad
+↓good
−↑alone
baby +↑
loved +↑
happy +↑
−↑stupid
−↑guilty
−↑sick
heart +↑
−↑scared
−↑lost
+↓music
+↓free
death −↓
life +↑
family +↑
+↓christmas
cold −↓
−↑upset
friend +↑
dead −↓
Per word average happiness shift δhavg,r (%)
Word
rank
r
Tre f: Male (havg=5.91)Tcom p: Female (havg=5.89)
Text s iz e :T re f T c omp
+↓ +↑
−↑ −↓
Balan ce :
−607 : +507
−100 0
100
101
102
103
∑r
i =1δ havg, i
COcoNuTS@networksvox
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Outline
Introduction
Measurement
HappinessSome motivationMeasuring emotional contentHedonometerAnalysisSongsBlogsSOTUGeographyMovementOther Emotions
References
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HappinessSome motivation
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Presidential happiness:
1910 1930 1950 1970 1990 20105.5
5.6
5.7
5.8
5.9
6
6.1
6.2
6.3
6.4
6.5
The
odor
e R
oose
velt
Will
iam
How
ard
Taf
t
Woo
drow
Wils
on
War
ren
G. H
ardi
ng Cal
vin
Coo
lidge
Her
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ver
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nklin
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oose
velt
Har
ry S
. Tru
man
Dw
ight
D. E
isen
how
er
John
F. K
enne
dy
Lynd
on B
. Joh
nson
Ric
hard
Nix
on
Ger
ald
R. F
ord
Jim
my
Car
ter
Ron
ald
Rea
gan
Geo
rge
Bus
h
Will
iam
J. C
linto
n
Geo
rge
W. B
ush
year
v avg
Presidential happiness:
1790 1810 1830 1850 1870 1890 1910 1930 1950 1970 1990 20105.55.65.75.85.9
66.16.26.36.46.5
Geo
rge
Was
hing
ton
John
Ada
ms
Tho
mas
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fers
on Jam
es M
adis
on
Jam
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And
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Mar
tin v
an B
uren
John
Tyl
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mes
K. P
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Tay
lor
Mill
ard
Fill
mor
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rank
lin P
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mes
Buc
hana
nA
brah
am L
inco
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ndre
w J
ohns
on
Uly
sses
S. G
rant
Rut
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ord
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ayes
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ster
A. A
rthu
r Ben
jam
inH
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on
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ver
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nd
Will
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. Har
ding
Cal
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. For
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r
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ald
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gan
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rge
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J. C
linto
n
Geo
rge
W. B
ush
year
vale
nce
(v)
1790 1800 1810 1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 20105.2
5.4
5.6
5.8
6
6.2
6.4
6.6
6.8
7
7.2
Geo
rge
Was
hing
ton
[1]
Geo
rge
Was
hing
ton
[2]
John
Ada
ms
Tho
mas
Jef
fers
on [1
]
Tho
mas
Jef
fers
on [2
]
Jam
es M
adis
on [1
]
Jam
es M
adis
on [2
]
Jam
es M
onro
e [1
]
Jam
es M
onro
e [2
]
John
Qui
ncy
Ada
ms
And
rew
Jac
kson
[1]
And
rew
Jac
kson
[2]
Mar
tin v
an B
uren
John
Tyl
er
Jam
es K
. Pol
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acha
ry T
aylo
rM
illar
d F
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Jam
es B
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nan
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aham
Lin
coln
And
rew
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sses
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rant
[1]
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sses
S. G
rant
[2]
Rut
herf
ord
B. H
ayes
Che
ster
A. A
rthu
r
Gro
ver
Cle
vela
nd [1
]
Ben
jam
in H
arris
on
Gro
ver
Cle
vela
nd [2
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Will
iam
McK
inle
y
The
odor
e R
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[1] The
odor
e R
oose
velt
[2]
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iam
How
ard
Taf
t
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Wils
on [1
]
Woo
drow
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on [2
] War
ren
G. H
ardi
ngC
alvi
n C
oolid
ge [1
]C
alvi
n C
oolid
ge [2
]
Her
bert
Hoo
ver
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nklin
D. R
oose
velt
[1]
Fra
nklin
D. R
oose
velt
[2]
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nklin
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oose
velt
[3]
Fra
nklin
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oose
velt
[4]
Har
ry S
. Tru
man
Dw
ight
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isen
how
er [1
]
Dw
ight
D. E
isen
how
er [2
]Jo
hn F
. Ken
nedy
Lynd
on B
. Joh
nson
[1]
Lynd
on B
. Joh
nson
[2]
Ric
hard
Nix
on
Ger
ald
R. F
ord
Jim
my
Car
ter
Ron
ald
Rea
gan
[1]
Ron
ald
Rea
gan
[2]
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rge
Bus
h
Will
iam
J. C
linto
n [1
]
Will
iam
J. C
linto
n [2
]
Geo
rge
W. B
ush
[1]
Geo
rge
W. B
ush
[2]
year
vale
nce
COcoNuTS@networksvox
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labMT 1.0:language assessment by Mechanical Turk
Four corpora: Twitter Google Books Music Lyrics New York
Times
5000 most frequently used words for each corpus. 10,222 words, 50 evaluations each, 1–9 scale: [27]
1 2 3 4 5 6 7 8 9
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valence word valence std dev twitter g-books nyt lyricsrank rank rank rank rank
1 laughter 8.50 0.93 3600 – – 17282 happiness 8.44 0.97 1853 2458 – 12303 love 8.42 1.11 25 317 328 234 happy 8.30 0.99 65 1372 1313 3755 laughed 8.26 1.16 3334 3542 – 23326 laugh 8.22 1.37 1002 3998 4488 6477 laughing 8.20 1.11 1579 – – 11228 excellent 8.18 1.10 1496 1756 3155 –9 laughs 8.18 1.16 3554 – – 285610 joy 8.16 1.06 988 2336 2723 80911 successful 8.16 1.08 2176 1198 1565 –12 win 8.12 1.08 154 3031 776 69413 rainbow 8.10 0.99 2726 – – 172314 smile 8.10 1.02 925 2666 2898 34915 won 8.10 1.22 810 1167 439 149316 pleasure 8.08 0.97 1497 1526 4253 139817 smiled 8.08 1.07 – 3537 – 224818 rainbows 8.06 1.36 – – – 421619 winning 8.04 1.05 1876 – 1426 364620 celebration 8.02 1.53 3306 – 2762 407021 enjoyed 8.02 1.53 1530 2908 3502 –22 healthy 8.02 1.06 1393 3200 3292 461923 music 8.02 1.12 132 875 167 37424 celebrating 8.00 1.14 2550 – – –25 congratulations 8.00 1.63 2246 – – –26 weekend 8.00 1.29 317 – 833 225627 celebrate 7.98 1.15 1606 – 3574 210828 comedy 7.98 1.15 1444 – 2566 –29 jokes 7.98 0.98 2812 – – 380830 rich 7.98 1.32 1625 1221 1469 890
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valence word valence std dev twitter g-books nyt lyricsrank rank rank rank rank
10193 violence 1.86 1.05 4299 1724 1238 201610194 cruel 1.84 1.15 2963 – – 144710195 cry 1.84 1.28 1028 3075 – 22610196 failed 1.84 1.00 2645 1618 1276 292010197 sickness 1.84 1.18 4735 – – 378210198 abused 1.83 1.31 – – – 458910199 tortured 1.82 1.42 – – – 469310200 fatal 1.80 1.53 – 4089 – 372410201 killings 1.80 1.54 – – 4914 –10202 murdered 1.80 1.63 – – – 479610203 war 1.80 1.41 468 175 291 46210204 kills 1.78 1.23 2459 – – 285710205 jail 1.76 1.02 1642 – 2573 161910206 terror 1.76 1.00 4625 4117 4048 237010207 die 1.74 1.19 418 730 2605 14310208 killing 1.70 1.36 1507 4428 1672 99810209 arrested 1.64 1.01 2435 4474 1435 –10210 deaths 1.64 1.14 – – 2974 –10211 raped 1.64 1.43 – – – 452810212 torture 1.58 1.05 3175 – – 312610213 died 1.56 1.20 1223 866 208 82610214 kill 1.56 1.05 798 2727 2572 43010215 killed 1.56 1.23 1137 1603 814 127310216 cancer 1.54 1.07 946 1884 796 380210217 death 1.54 1.28 509 307 373 43310218 murder 1.48 1.01 2762 3110 1541 105910219 terrorism 1.48 0.91 – – 3192 –10220 rape 1.44 0.79 3133 – 4115 297710221 suicide 1.30 0.84 2124 4707 3319 210710222 terrorist 1.30 0.91 3576 – 3026 –
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
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std dev word valence std dev twitter g-books nyt lyricsrank rank rank rank rank
1 fking 4.64 2.93 448 – – 6202 fkin 3.86 2.74 1077 – – 6883 fked 3.56 2.71 1840 – – 9044 pussy 4.80 2.66 2019 – – 9495 whiskey 5.72 2.64 – – – 22086 slut 3.57 2.63 – – – 40717 cigarettes 3.31 2.60 – – – 32798 fk 4.14 2.58 322 – – 1859 mortality 4.38 2.55 – 3960 – –10 cigarette 3.09 2.52 – – – 267811 motherfkers 2.51 2.47 – – – 146612 churches 5.70 2.46 – 2281 – –13 motherfking 2.64 2.46 – – – 291014 capitalism 5.16 2.45 – 4648 – –15 porn 4.18 2.43 1801 – – –16 summer 6.40 2.39 896 1226 721 59017 beer 5.92 2.39 839 4924 3960 141318 execution 3.10 2.39 – 2975 – –19 wines 6.28 2.37 – – 3316 –20 zombies 4.00 2.37 4708 – – –21 aids 4.28 2.35 2983 3996 1197 –22 capitalist 4.84 2.34 – 4694 – –23 revenge 3.71 2.34 – – – 276624 mcdonalds 5.98 2.33 3831 – – –25 beatles 6.44 2.33 3797 – – –26 islam 4.68 2.33 – 4514 – –27 pay 5.30 2.32 627 769 460 49928 alcohol 5.20 2.32 2787 2617 3752 360029 muthafkin 3.00 2.31 – – – 410730 christ 6.16 2.31 2509 909 4238 1526
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Twitter—living in the now:
0 2 4 6 8 10 12 14 16 18 20 22 240
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
hour of day (local time)
coun
t fra
ctio
n
breakfast
lunch
dinner
Quantifying the quotidian.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
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Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
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Twitter—living in the now:
0 2 4 6 8 10 12 14 16 18 20 22 240
0.01
0.02
0.03
0.04
0.05
0.06
0.07
hour of day (local time)
coun
t fra
ctio
n
hungry
starving
food
eat
chicken
Makes the unexpected believable...
The happiest distribution:
2 4 6 10 26 100 14010
0
102
104
106
Number of Letters (String Length)
Fre
quen
cy
haha
ha hahaha
hahahahaha
hahahahahahahahahahahahaha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
haha
Work by Tyler Gray et al., unpublished.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
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Songs
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Happiness
Introduction
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Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
.....
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Individual tweets have all kinds of potentialimpact:
Meme come true: Fleetwood Mac re-enter UScharts thanks to Twitter post (2018/04/04)
Trump’s tweets (Amazon is a buy?)
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Mentions of CIA on Twitter, end of 2011:
See story here for example [slate].
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The daily unravelling of the human mind:
midnight noon midnight noon midnight noon midnight noon midnight noon midnight noon midnight noon midnight noon midnight5.9
5.95
6
6.05
6.1
6.15
Local time
Aver
age
happin
ess
havg 2010
Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday
“Social Scientists wade into the Tweet stream” byGreg Miller,Science, 333, 1814–1815, 2011 [28]
COcoNuTS@networksvox
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Introduction
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Songs
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The daily unravelling of the human mind:
midnight noon midnight noon midnight noon midnight noon midnight noon midnight noon midnight noon midnight noon midnight5.9
5.95
6
6.05
6.1
6.15
Local time
Aver
age
happin
ess
havg 2010
Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday
0 2 4 6 8 10 12 14 16 18 20 22 240
0.01
0.02
0.03
0.04
0.05
0.06
hour of day (local time)
coun
t (%
)
“Social Scientists wade into the Tweet stream” byGreg Miller,Science, 333, 1814–1815, 2011 [28]
COcoNuTS@networksvox
Happiness
Introduction
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−10 −5 0 5 10
1
5
10
15
20
25
30
35
40
45
50
love +↑no −↓
haha +↑party +↑
fun +↑saturday +↑
+↓newweekend +↑not −↓happy +↑dont −↓
−↑lasthahaha +↑
−↑bored−↑drunk
live +↑die −↓friends +↑game +↑con −↓movie +↑cant −↓
−↑fightbirthday +↑
+↓googlegreat +↑sunday +↑family +↑beautiful +↑beach +↑home +↑
+↓lunchsick −↓shopping +↑playing +↑
−↑don’tamazing +↑bad −↓awesome +↑homework −↓wedding +↑
−↑hangover−↑miss+↓free
shit −↓court −↓nice +↑won +↑
+↓schoolmovies +↑
Per word average happiness shift δhavg,r (%)
Wor
dra
nkr
Tref: Tuesdays (havg=6.03)Tcomp: Saturdays (havg=6.06)
Text size:Tref Tcomp
+↓ +↑
−↑ −↓
Balance:
−87 : +187
0 100
100
101
102
103
104
∑r
i=1 δhavg,i
COcoNuTS@networksvox
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Introduction
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Text element and context correlate in happinessscores: Compare ambient happiness with text element
happiness. Spearman correlation coefficient:𝑟𝑠 ≃ 0.79, 𝑝-value < 10−10. An on-average result: says nothing about any
individual sentence. Extra random piece: stemming is fallible.
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug5.8
5.9
6
6.1
6.2
6.3
6.4
09/29
10/31
11/27
12/24
12/25
12/31
01/0102/14
04/12
04/27
06/21
06/25
07/04
08/0609/14
10/31
11/2612/24
12/25
12/31
01/01
02/14
02/27
04/0405/09
05/24
06/20
06/27
07/04
11/25
12/24
12/25
12/31
01/0102/14
03/11
04/24
04/29
05/02
05/08
06/19
07/05
07/23 08/0808/23
ave
rage
happin
ess
havg
A
2008— 2009— 2010— 2011—MondayTuesdayWednesdayThursdayFridaySaturdaySunday
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug
300
400
500
600
700
Sim
pso
nle
xica
lsize
NS
B
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug0
1
2
3
date
word
count
(x10
7)
C
−20 −10 0 10 20
1
5
10
15
20
25
30
35
40
45
50
−↑bailout−↑bill
−↑down−↑failed
−↑notlast −↓
−↑no−↑fail
−↑fails−↑blame
+↓love−↑failure
−↑bad−↑don’t
−↑against−↑die
−↑rejected−↑depression
−↑crisismoney +↑
+↓sunday+↓fun
+↓party+↓game
weekend +↑+↓won
old −↓−↑worst+↓great−↑panic
+↓awesome−↑didn’t
+↓google+↓saturday
billion +↑−↑hurt+↓win
cancer −↓−↑sick
−↑problem−↑crash+↓friday−↑falling
house +↑+↓home−↑killed
−↑fearmiss −↓
−↑gossip−↑poor
Per word average happiness shift δhavg,r (%)
Wor
dra
nkr
Tref: 7 days before and after (havg=6.00)Tcomp: Monday, 2008/09/29 (havg=5.95)
Bailout of the U.S. financial system:
A
Text size:Tref Tcomp
+↓ +↑
−↑ −↓
Balance:
−165 : +65
−100 0
100
101
102
103
104
∑r
i=1 δhavg,i
−30 −20 −10 0 10 20 30
1
5
10
15
20
25
30
35
40
45
50
wedding +↑dead −↓
dont −↓death −↓beautiful +↑hate −↓kiss +↑prince +↑
+↓easter+↓happy
no −↓princess +↑never −↓shit −↓killed −↓not −↓dress +↑real +↑
+↓me+↓good
+↓youweekend +↑friday +↑party +↑
+↓chocolate+↓love+↓win
married +↑+↓game
live +↑bad −↓congrats +↑amazing +↑kill −↓ill −↓nigga −↓wow +↑died −↓
+↓lifeass −↓hell −↓gorgeous +↑congratulations +↑
+↓lolcouple +↑
+↓friendskissed +↑she +↑killing −↓
+↓haha
Per word average happiness shift δhavg,r (%)
Wor
dra
nk
r
Tref: 7 days before and after (havg=5.98)Tcomp: Friday, 2011/04/29 (havg=6.04)
Royal Wedding of Prince William & Catherine Middleton:
B
Text size:Tref Tcomp
+↓ +↑
−↑ −↓
Balance:
−68 : +168
0 100
100
101
102
103
104
∑r
i=1 δhavg,i
−20 −10 0 10 20
1
5
10
15
20
25
30
35
40
45
50
−↑dead−↑death
−↑killed+↓love
−↑not−↑kill
−↑died−↑killing+↓happy
−↑saddam+↓haha
+↓me−↑war
+↓mothers−↑terrorist
−↑bad−↑pakistan
−↑buriedno −↓
−↑hussein+↓hahaha
−↑terror+↓like
+↓wedding−↑terrorism
−↑enemy+↓lol
−↑shotcelebrating +↑ill −↓
−↑hellcelebrate +↑
+↓mom−↑loss
−↑mournamerica +↑
+↓you−↑deaths
1st +↑sea +↑usa +↑
+↓beautifulpeace +↑
+↓fun−↑evil
+↓good−↑attack
we +↑−↑breaking
−↑burial
Per word average happiness shift δhavg,r (%)
Wor
dra
nkr
Tref: 7 days before and after (havg=5.98)Tcomp: Monday, 2011/05/02 (havg=5.89)
Death of Osama Bin Laden:
C
Text size:Tref Tcomp
+↓ +↑
−↑ −↓
Balance:
−143 : +43
−100 0
100
101
102
103
104
∑r
i=1 δhavg,i
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Introduction
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Ambient happiness:Word h
(amb)avg Total Tweets h
(norm)avg
1. happy +0.430 1.65e+07 (13) +1.104 (1)2. Christmas +0.404 4.89e+06 (35) +0.953 (3)3. vegan +0.315 1.84e+05 (90) -0.015 (46)4. :) +0.274 1.04e+07 (20) +0.630 (12)5. family +0.251 5.01e+06 (32) +0.716 (7)6. :-) +0.228 1.67e+06 (60) +0.560 (16)7. our +0.207 1.41e+07 (16) +0.159 (33)8. win +0.204 7.98e+06 (26) +0.924 (4)9. vacation +0.200 9.35e+05 (67) +0.817 (5)10. party +0.170 6.44e+06 (29) +0.679 (9)11. love +0.164 4.67e+07 (6) +0.977 (2)12. friends +0.155 7.67e+06 (27) +0.685 (8)13. hope +0.149 1.18e+07 (18) +0.515 (19)14. coffee +0.147 2.80e+06 (46) +0.518 (18)15. cash +0.146 1.28e+06 (63) +0.601 (14)16. sun +0.144 2.39e+06 (52) +0.737 (6)17. income +0.137 5.10e+05 (76) +0.621 (13)18. summer +0.135 3.00e+06 (43) +0.221 (29)19. church +0.131 1.81e+06 (58) -0.016 (47)20. Valentine +0.127 2.47e+05 (84) +0.593 (15)21. Stephen Colbert +0.126 2.38e+04 (99) +0.001 (45)22. USA +0.113 2.16e+06 (54) +0.325 (26)23. ! +0.106 3.44e+06 (40) +0.195 (31)24. winter +0.101 1.26e+06 (64) +0.050 (43)25. God +0.099 8.58e+06 (25) +0.468 (20)26. hot +0.095 7.12e+06 (28) -0.172 (54)27. ;) +0.094 2.61e+06 (48) +0.326 (25)28. Jesus +0.094 2.03e+06 (56) +0.247 (28)29. today +0.092 2.56e+07 (9) +0.126 (36)30. kiss +0.072 1.70e+06 (59) +0.632 (11)31. yes +0.056 1.16e+07 (19) +0.321 (27)32. tomorrow +0.054 1.04e+07 (21) +0.086 (38)33. you +0.052 1.73e+08 (3) +0.111 (37)34. heaven +0.041 7.42e+05 (71) +0.674 (10)35. ;-) +0.041 9.39e+05 (66) +0.395 (23)36. we +0.035 3.91e+07 (7) +0.146 (34)37. yesterday +0.033 3.08e+06 (42) -0.168 (53)38. dark +0.031 1.58e+06 (61) -0.766 (81)39. ? +0.030 2.32e+06 (53) -0.503 (68)40. RT +0.028 3.39e+08 (1) -0.443 (66)41. Michael Jackson +0.018 8.26e+05 (70) -0.213 (59)42. night +0.014 1.71e+07 (12) +0.074 (40)43. life +0.012 1.40e+07 (17) +0.422 (22)44. health -0.000 2.58e+06 (50) +0.447 (21)45. sex -0.008 3.55e+06 (39) +0.542 (17)46. work -0.010 1.84e+07 (11) -0.174 (56)47. girl -0.010 1.01e+07 (22) +0.331 (24)48. boy -0.026 4.93e+06 (33) +0.062 (41)49. I -0.048 3.08e+08 (2) -0.062 (49)50. commute -0.048 9.01e+04 (94) -0.206 (57)
Word h(amb)avg Total Tweets h
(norm)avg
51. snow -0.051 2.60e+06 (49) +0.083 (39)52. Jon Stewart -0.052 5.21e+04 (97) -0.024 (48)53. school -0.056 9.26e+06 (24) +0.050 (42)54. Lehman Brothers -0.078 8.50e+03 (100) -0.721 (79)55. them -0.090 1.54e+07 (15) -0.280 (60)56. right -0.090 1.92e+07 (10) +0.126 (35)57. woman -0.115 2.54e+06 (51) +0.202 (30)58. left -0.118 4.89e+06 (34) -0.383 (63)59. me -0.119 1.44e+08 (4) +0.160 (32)60. election -0.127 5.60e+05 (75) -0.306 (61)61. Sarah Palin -0.128 2.26e+05 (87) -0.681 (76)62. no -0.132 9.51e+07 (5) -1.415 (90)63. rain -0.134 3.23e+06 (41) +0.050 (44)64. climate -0.135 3.64e+05 (80) -0.160 (51)65. gay -0.152 2.73e+06 (47) -0.552 (72)66. lose -0.157 2.06e+06 (55) -1.181 (86)67. they -0.159 2.74e+07 (8) -0.208 (58)68. oil -0.162 1.38e+06 (62) -0.411 (65)69. cold -0.162 3.67e+06 (36) -0.546 (71)70. I feel -0.173 5.17e+06 (31) -0.129 (50)71. man -0.175 1.59e+07 (14) -0.163 (52)72. Republican -0.181 2.30e+05 (86) -0.539 (70)73. sad -0.187 3.56e+06 (38) -1.366 (89)74. gas -0.193 1.02e+06 (65) -0.471 (67)75. economy -0.203 6.09e+05 (73) -0.525 (69)76. Obama -0.205 2.98e+06 (44) -0.173 (55)77. Democrat -0.226 9.32e+04 (93) -0.384 (64)78. Congress -0.231 3.92e+05 (79) -0.580 (74)79. hell -0.250 6.27e+06 (30) -1.551 (96)80. sick -0.262 3.58e+06 (37) -1.630 (97)81. Muslim -0.262 2.15e+05 (88) -0.569 (73)82. war -0.270 1.96e+06 (57) -2.040 (100)83. Pope -0.277 1.52e+05 (91) -0.316 (62)84. hate -0.282 9.65e+06 (23) -1.520 (94)85. Glenn Beck -0.282 1.14e+05 (92) -0.776 (82)86. Islam -0.299 1.87e+05 (89) -0.710 (78)87. George Bush -0.333 3.23e+04 (98) -0.747 (80)88. Goldman Sachs -0.337 5.27e+04 (96) -0.984 (84)89. depressed -0.339 2.81e+05 (82) -1.541 (95)90. Senate -0.340 4.48e+05 (78) -0.601 (75)91. BP -0.355 5.82e+05 (74) -0.902 (83)92. gun -0.367 6.81e+05 (72) -1.476 (93)93. drugs -0.382 5.10e+05 (77) -1.452 (91)94. headache -0.437 8.57e+05 (69) -1.881 (98)95. :-( -0.455 3.40e+05 (81) -1.174 (85)96. :( -0.472 2.89e+06 (45) -1.288 (88)97. Afghanistan -0.703 2.74e+05 (83) -1.458 (92)98. mosque -0.709 6.98e+04 (95) -0.694 (77)99. flu -0.735 9.01e+05 (68) -1.912 (99)100. Iraq -0.773 2.39e+05 (85) -1.282 (87)
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.....
.92 of 155
Simpson lexical size, 𝑁S:Word h
(amb)avg Total Tweets h
(norm)avg
1. happy +0.430 1.65e+07 (13) +1.104 (1)2. Christmas +0.404 4.89e+06 (35) +0.953 (3)3. vegan +0.315 1.84e+05 (90) -0.015 (46)4. :) +0.274 1.04e+07 (20) +0.630 (12)5. family +0.251 5.01e+06 (32) +0.716 (7)6. :-) +0.228 1.67e+06 (60) +0.560 (16)7. our +0.207 1.41e+07 (16) +0.159 (33)8. win +0.204 7.98e+06 (26) +0.924 (4)9. vacation +0.200 9.35e+05 (67) +0.817 (5)10. party +0.170 6.44e+06 (29) +0.679 (9)11. love +0.164 4.67e+07 (6) +0.977 (2)12. friends +0.155 7.67e+06 (27) +0.685 (8)13. hope +0.149 1.18e+07 (18) +0.515 (19)14. coffee +0.147 2.80e+06 (46) +0.518 (18)15. cash +0.146 1.28e+06 (63) +0.601 (14)16. sun +0.144 2.39e+06 (52) +0.737 (6)17. income +0.137 5.10e+05 (76) +0.621 (13)18. summer +0.135 3.00e+06 (43) +0.221 (29)19. church +0.131 1.81e+06 (58) -0.016 (47)20. Valentine +0.127 2.47e+05 (84) +0.593 (15)21. Stephen Colbert +0.126 2.38e+04 (99) +0.001 (45)22. USA +0.113 2.16e+06 (54) +0.325 (26)23. ! +0.106 3.44e+06 (40) +0.195 (31)24. winter +0.101 1.26e+06 (64) +0.050 (43)25. God +0.099 8.58e+06 (25) +0.468 (20)26. hot +0.095 7.12e+06 (28) -0.172 (54)27. ;) +0.094 2.61e+06 (48) +0.326 (25)28. Jesus +0.094 2.03e+06 (56) +0.247 (28)29. today +0.092 2.56e+07 (9) +0.126 (36)30. kiss +0.072 1.70e+06 (59) +0.632 (11)31. yes +0.056 1.16e+07 (19) +0.321 (27)32. tomorrow +0.054 1.04e+07 (21) +0.086 (38)33. you +0.052 1.73e+08 (3) +0.111 (37)34. heaven +0.041 7.42e+05 (71) +0.674 (10)35. ;-) +0.041 9.39e+05 (66) +0.395 (23)36. we +0.035 3.91e+07 (7) +0.146 (34)37. yesterday +0.033 3.08e+06 (42) -0.168 (53)38. dark +0.031 1.58e+06 (61) -0.766 (81)39. ? +0.030 2.32e+06 (53) -0.503 (68)40. RT +0.028 3.39e+08 (1) -0.443 (66)41. Michael Jackson +0.018 8.26e+05 (70) -0.213 (59)42. night +0.014 1.71e+07 (12) +0.074 (40)43. life +0.012 1.40e+07 (17) +0.422 (22)44. health -0.000 2.58e+06 (50) +0.447 (21)45. sex -0.008 3.55e+06 (39) +0.542 (17)46. work -0.010 1.84e+07 (11) -0.174 (56)47. girl -0.010 1.01e+07 (22) +0.331 (24)48. boy -0.026 4.93e+06 (33) +0.062 (41)49. I -0.048 3.08e+08 (2) -0.062 (49)50. commute -0.048 9.01e+04 (94) -0.206 (57)
Word h(amb)avg Total Tweets h
(norm)avg
51. snow -0.051 2.60e+06 (49) +0.083 (39)52. Jon Stewart -0.052 5.21e+04 (97) -0.024 (48)53. school -0.056 9.26e+06 (24) +0.050 (42)54. Lehman Brothers -0.078 8.50e+03 (100) -0.721 (79)55. them -0.090 1.54e+07 (15) -0.280 (60)56. right -0.090 1.92e+07 (10) +0.126 (35)57. woman -0.115 2.54e+06 (51) +0.202 (30)58. left -0.118 4.89e+06 (34) -0.383 (63)59. me -0.119 1.44e+08 (4) +0.160 (32)60. election -0.127 5.60e+05 (75) -0.306 (61)61. Sarah Palin -0.128 2.26e+05 (87) -0.681 (76)62. no -0.132 9.51e+07 (5) -1.415 (90)63. rain -0.134 3.23e+06 (41) +0.050 (44)64. climate -0.135 3.64e+05 (80) -0.160 (51)65. gay -0.152 2.73e+06 (47) -0.552 (72)66. lose -0.157 2.06e+06 (55) -1.181 (86)67. they -0.159 2.74e+07 (8) -0.208 (58)68. oil -0.162 1.38e+06 (62) -0.411 (65)69. cold -0.162 3.67e+06 (36) -0.546 (71)70. I feel -0.173 5.17e+06 (31) -0.129 (50)71. man -0.175 1.59e+07 (14) -0.163 (52)72. Republican -0.181 2.30e+05 (86) -0.539 (70)73. sad -0.187 3.56e+06 (38) -1.366 (89)74. gas -0.193 1.02e+06 (65) -0.471 (67)75. economy -0.203 6.09e+05 (73) -0.525 (69)76. Obama -0.205 2.98e+06 (44) -0.173 (55)77. Democrat -0.226 9.32e+04 (93) -0.384 (64)78. Congress -0.231 3.92e+05 (79) -0.580 (74)79. hell -0.250 6.27e+06 (30) -1.551 (96)80. sick -0.262 3.58e+06 (37) -1.630 (97)81. Muslim -0.262 2.15e+05 (88) -0.569 (73)82. war -0.270 1.96e+06 (57) -2.040 (100)83. Pope -0.277 1.52e+05 (91) -0.316 (62)84. hate -0.282 9.65e+06 (23) -1.520 (94)85. Glenn Beck -0.282 1.14e+05 (92) -0.776 (82)86. Islam -0.299 1.87e+05 (89) -0.710 (78)87. George Bush -0.333 3.23e+04 (98) -0.747 (80)88. Goldman Sachs -0.337 5.27e+04 (96) -0.984 (84)89. depressed -0.339 2.81e+05 (82) -1.541 (95)90. Senate -0.340 4.48e+05 (78) -0.601 (75)91. BP -0.355 5.82e+05 (74) -0.902 (83)92. gun -0.367 6.81e+05 (72) -1.476 (93)93. drugs -0.382 5.10e+05 (77) -1.452 (91)94. headache -0.437 8.57e+05 (69) -1.881 (98)95. :-( -0.455 3.40e+05 (81) -1.174 (85)96. :( -0.472 2.89e+06 (45) -1.288 (88)97. Afghanistan -0.703 2.74e+05 (83) -1.458 (92)98. mosque -0.709 6.98e+04 (95) -0.694 (77)99. flu -0.735 9.01e+05 (68) -1.912 (99)100. Iraq -0.773 2.39e+05 (85) -1.282 (87)
COcoNuTS@networksvox
Happiness
Introduction
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Ambient happiness:
09/09/08 12/01/08 03/01/09 06/01/09 09/01/09−1.2
−1
−0.8
−0.6
−0.4
−0.2
0
0.2
0.4
date
h avg
(am
b)
HappyTea Party
:)!
:(Afghanistan
Sad
09/09/08 12/01/08 03/01/09 06/01/09 09/01/09−5
−4
−3
−2
−1
date
log 10
rel f
req
A
B
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0 20 40 60 80 1000
10
20
30
40
50
60
70
80
90
100S
imps
on le
xica
l siz
e (N
S)
rank
ambient happiness (havg(amb)) rank
COcoNuTS@networksvox
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10/01/09 12/01/09 02/01/10 04/01/10 06/01/10−1
−0.8
−0.6
−0.4
−0.2
0
0.2 A
date
h avg
(am
b)
Tiger Woods
09/01/09 12/01/09 03/01/10 05/31/10−5
−4
−3
date
log 10
rel f
req
01/01/10 03/01/10 05/01/10 07/01/10−1
−0.8
−0.6
−0.4
−0.2
0
0.2 B
date
h avg
(am
b)
BP
12/01/09 03/01/10 05/31/10−5
−4
−3
date
log 10
rel f
req
−20 0 20
1
5
10
15
20
25
−↑accident−↑crash
−↑injuredno −↓
−↑scandal+↓love
−↑hospital−↑cheating
wife +↑−↑alleged
car +↑+↓me
santa +↑−↑cheated
ill −↓−↑cheat
sex +↑+↓haha−↑alone
−↑not+↓happy
−↑breaking−↑divorce
movie +↑+↓good
Per word average happiness shi ft δhavg, r (%)
Word
rank
r
Tref: All Tweets (havg=6.06)Tcomp: Tiger Woods (havg=5.74)
Text size:Tre f Tcomp
+↓ +↑
−↑ −↓
Ba lance:−188 : +88
−100 0
100
101
102
103
104
C
∑r
i=1δhavg, i
−20 −10 0 10 20
1
5
10
15
20
25
−↑disasterno −↓
−↑down−↑shut
−↑kill+↓love−↑fake
−↑stop+↓me
+↓haha−↑not+↓lol
ill −↓+↓hahaha
dont −↓−↑died
billion +↑con −↓
−↑blame−↑criminal
+↓happy−↑damage
−↑costs−↑fails
−↑crisis
Per word average happiness shi ft δhavg, r (%)
Word
rank
r
Tref: All Tweets (havg=6.04)Tcomp: BP (havg=5.57)
Text size:Tre f Tcomp
+↓ +↑
−↑ −↓
Ba lance:−167 : +67
−100 0
100
101
102
103
104
D
∑r
i=1δhavg, i
COcoNuTS@networksvox
Happiness
Introduction
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HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
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10/01/09 12/01/09 02/01/10 04/01/10 06/01/10−1
−0.8
−0.6
−0.4
−0.2
0
0.2 A
date
h avg
(am
b)
Tiger Woods
09/01/09 12/01/09 03/01/10 05/31/10−5
−4
−3
date
log 10
rel f
req
01/01/10 03/01/10 05/01/10 07/01/10−1
−0.8
−0.6
−0.4
−0.2
0
0.2 B
date
h avg
(am
b)
BP
12/01/09 03/01/10 05/31/10−5
−4
−3
date
log 10
rel f
req
−20 0 20
1
5
10
15
20
25
−↑accident−↑crash
−↑injuredno −↓
−↑scandal+↓love
−↑hospital−↑cheating
wife +↑−↑alleged
car +↑+↓me
santa +↑−↑cheated
ill −↓−↑cheat
sex +↑+↓haha−↑alone
−↑not+↓happy
−↑breaking−↑divorce
movie +↑+↓good
Per word average happiness shi ft δhavg, r (%)
Word
rank
r
Tref: All Tweets (havg=6.06)Tcomp: Tiger Woods (havg=5.74)
Text size:Tre f Tcomp
+↓ +↑
−↑ −↓
Ba lance:−188 : +88
−100 0
100
101
102
103
104
C
∑r
i=1δhavg, i
−20 −10 0 10 20
1
5
10
15
20
25
−↑disasterno −↓
−↑down−↑shut
−↑kill+↓love−↑fake
−↑stop+↓me
+↓haha−↑not+↓lol
ill −↓+↓hahaha
dont −↓−↑died
billion +↑con −↓
−↑blame−↑criminal
+↓happy−↑damage
−↑costs−↑fails
−↑crisis
Per word average happiness shi ft δhavg, r (%)
Word
rank
r
Tref: All Tweets (havg=6.04)Tcomp: BP (havg=5.57)
Text size:Tre f Tcomp
+↓ +↑
−↑ −↓
Ba lance:−167 : +67
−100 0
100
101
102
103
104
D
∑r
i=1δhavg, i
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
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02/01/10 04/01/10 06/01/10 08/01/10−1
−0.8
−0.6
−0.4
−0.2
0
0.2 A
date
h avg
(am
b)
Pope
03/01/10 05/31/10−5
−4
−3
date
log 10
rel f
req
11/01/08 01/01/09 03/01/09 05/01/09−1
−0.8
−0.6
−0.4
−0.2
0
0.2 B
date
h avg
(am
b)
Israel
09/09/08 12/01/08 03/01/09−5
−4
−3
date
log 10
rel f
req
−50 0 50
1
5
10
15
20
25
−↑abuse−↑scandal
sex +↑−↑victims
−↑arrestno −↓
−↑against+↓love
−↑arrestedchild +↑
−↑not−↑resignation
+↓me+↓haha−↑crisis
+↓loldont −↓
−↑crimes−↑attacks
−↑accused−↑deaf
−↑down+↓hahaha
−↑evil−↑rape
Per word average happiness shi ft δhavg, r (%)
Word
rank
r
Tref: All Tweets (havg=6.04)Tcomp: Pope (havg=5.40)
Text size:Tre f Tcomp
+↓ +↑
−↑ −↓
Ba lance:−157 : +57
−100 0
100
101
102
103
104
C
∑r
i=1δhavg, i
−10 0 10
1
5
10
15
20
25
−↑war−↑fired
−↑against−↑killed
−↑attacks−↑attack
−↑offensive−↑fire
−↑crimespeace +↑
−↑no−↑not−↑die
−↑killing−↑stop+↓love
−↑bombs−↑fighting
−↑kill−↑kills
−↑conflict−↑death
−↑terrorist−↑weapons−↑bombing
Per word average happiness shi ft δhavg, r (%)
Word
rank
r
Tref: All Tweets (havg=6.05)Tcomp: Israel (havg=5.30)
Text size:Tre f Tcomp
+↓ +↑
−↑ −↓
Ba lance:−131 : +31
−100 0
100
101
102
103
104
D
∑r
i=1δhavg, i
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
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Public Opinion Polling with Twitter
Emily M. Cody,1, ∗ Andrew J. Reagan,1, † Peter Sheridan Dodds,1, ‡ and Christopher M. Danforth1, §
1Department of Mathematics & Statistics, Vermont Complex Systems Center,
Computational Story Lab, & the Vermont Advanced Computing Core,
The University of Vermont, Burlington, VT 05401.
(Dated: August 9, 2016)
Solicited public opinion surveys reach a limited subpopulation of willing participants and areexpensive to conduct, leading to poor time resolution and a restricted pool of expert-chosen surveytopics. In this study, we demonstrate that unsolicited public opinion polling through sentimentanalysis applied to Twitter correlates well with a range of traditional measures, and has predictivepower for issues of global importance. We also examine Twitter’s potential to canvas topics seldomsurveyed, including ideas, personal feelings, and perceptions of commercial enterprises. Two of ourmajor observations are that appropriately filtered Twitter sentiment (1) predicts President Obama’sjob approval three months in advance, and (2) correlates well with surveyed consumer sentiment.To make possible a full examination of our work and to enable others’ research, we make public over10,000 data sets, each a seven-year series of daily word counts for tweets containing a frequentlyused search term.
I. INTRODUCTION
Public opinion data can be used to determine pub-lic awareness, to predict outcomes of events, and to infercharacteristics of human behaviors. Indeed, readily avail-able public opinion data is valuable to researchers, poli-cymakers, marketers, and many other groups, but is dif-ficult to generate. Solicited polls can be expensive, pro-hibitively time consuming, and may only reach a limitednumber of people on a limited number of days. Polling ac-curacy evidently relies on accessing representative popu-lations and high response rates. Poor temporal samplingwill weaken any poll’s value as individual opinions varyin time and in response to social influence[12, 43].
With the continued rise of social media as a communi-cation platform, the ability to construct unsolicited pub-lic opinion polls has become a possibility for researchersthough parsing of massive text-based datasets. Socialmedia provides extraordinary access to public expressionsin real time, and has been shown to play a role in humanbehavior [23].
With its open platform, Twitter has proved to be aboon for many research enterprises [37], having been usedto explore a variety of social and linguistic phenomena[10, 30, 31]; harnessed as a data source to create anearthquake reporting system in Japan [42]; made pos-sible detection of influenza outbreaks [7]; and used toanalyze overall public health [38]. Predictions made us-ing Twitter have focused on elections [22, 44], the spreadof disease [41], crime [46], and the stock market [35].These studies demonstrate a proof-of-concept, avoidingthe more difficult task of building operational systemsfor continued forecasting.
∗ emily.cody@uvm.edu† andrew.reagan@uvm.edu‡ peter.dodds@uvm.edu§ chris.danforth@uvm.edu
We must be clear that for all its promise, predictionvia social media is difficult. Indeed, we have seen a num-ber of high profile failures such as Google Flu trends [29]and various attempts to predict election outcomes [21].OpinionFinder was used in [9] to evaluate tweets contain-ing direct expressions of emotion as in ‘I feel’, or ‘I amfeeling’, and shown not to have predictive power for thestock market
Despite limitations which we address later in Sec. IV,Twitter data reveals an unprecedented view of humanbehavior and opinion related to major issues of globalimportance [32]. In a previous study [14], we analyzedthe sentiment surrounding climate change conversationon Twitter. We discovered that sentiment varies in re-sponse to climate change news and events, and that theconversation is dominated by activists. Another studyby Helmuth et al. analyzed tweets by United States Sen-ators to determine which research oriented science orga-nizations and which senators are best at getting science-related findings into the hands of the general public [24].Twitter is also often used to analyze public opinion ofpolitical issues [8, 17, 45], and in several previous worksas an opinion polling resource. In an application usingneural networks called TrueHappiness, users enter one of300,000 words to obtain a sentiment estimation based onthis word’s usage in a massive Wikipedia data set, andon previously collected sentiment scores for 10,222 wordson Amazon’s Mechanical Turk [16, 18], hereafter referredto as the labMT word set In another application calledRACCOON, users are invited to enter a query term anda rough sketch to obtain words or phrases on Twitterthat correlate well with the inputs [6]. Google Correlateis a similar tool that discovers Google searches for termsor phrases that match well with real-world time series [3].Financial term searches from Google Trends was shownby Preis et al. [39] to correlate with Dow Jones economicindices.
We argue that Twitter is a better source for opin-ion mining than Wikipedia, used in TrueHappiness, due
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“Public opinion polling with Twitter”Cody et al.,Available online athttps://arxiv.org/abs/1608.02024, 2016. [4]
COcoNuTS@networksvox
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Introduction
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Twitter’s Feels predict Obama’s Approval Rating:
−5 0 5
1
5
10
15
20
25
30
35
40
45
50
−↑no−↑war−↑iraq
−↑against−↑not
−↑illegal+↓new
−↑crisis−↑fight
tax −↓−↑threat−↑killed+↓hope
+↓first−↑terrorist
−↑deathbill −↓
−↑worstbailout −↓
−↑stop−↑con+↓love
+↓goodamerica +↑
−↑destroy−↑killing−↑attack
−↑lawsuit−↑never
vacation +↑taxes −↓
+↓great−↑crying
−↑breakingafghanistan −↓
−↑hatedie −↓
−↑violencethanks +↑fail −↓deficit −↓
−↑doesn’t−↑shit−↑kill
−↑courtlast −↓
−↑bombinglike +↑
−↑murderyou +↑
Per word average happiness shif t δh av g, r (%)
Wordrankr
T r e f : Obama Q1 (havg=5.92)Tcom p: Obama Q23 (havg=5.53)
Text si z e :T r e f T c omp
+↓ +↑
−↑ −↓
Balanc e :
−154 : +54
−100 0
100
101
102
103
104
∑ri=1δ hav g, i
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Ambient happiness for “Obama”:
Birthday
Presidential election Presidential election
Nobel Prize
H1N1 emergency
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Ambient happiness for “Walmart”:
Free gift card
Employee trampled Tracy Morgan sues
Black Friday
A
Shootings
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Ambient happiness for “snow”:
B C
Jon Snow dies on GOT
g
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Dunbar number action:
0 1 2 3 4 5 66.3
6.35
6.4
6.45
6.5
mea
n va
lenc
e v
0 1 2 3 4 5 6250
260
270
280
290
300
310
N e
q
0 1 2 3 4 5 65
6
7
8
log10
follower number per tweet
log 10
cou
nt
# ANEW words
Early Twitter data—follower counts are not worthso much.
Unpublished.
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392 C.A. Bliss et al. / Journal of Computational Science 3 (2012) 388–397
Fig. 5. A visualization of the 162,445 nodes in the reciprocal reply network for the week beginning December 9, 2008 (Week 14) is depicted. Node colors represent connected
components, a total of 15,342, with the giant component (shown in blue) comprising 76 % of all nodes. The size of each node is proportional to its degree. The visualization
was made using Gephi [39]. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
from a power law using the Kolmogorov–Smirnov test and find no
evidence against the null hypothesis for the week (D = 2.28 × 10−2,
p = 0.095, n = 203,852). We find the same exponent and statistically
stronger evidence of a power law for a sample month (see Fig. A1).
This suggests that these distributions’ tails may be fit by a power
law.
3.2. Measuring happiness
The application of the hedonometer gives reasonable results
when applied to a large body of text, but can be misleading when
applied to smaller units of language [11]. To provide a sense of how
sensitive this measure is to the number of labMT words posted
by users, we sampled happiness–happiness pairs, (hvi, hvj
) whose
respective users, vi and vj , had posted at least total labMT words
during a sample week (week beginning January 27, 2009). For these
users, we compute happiness assortativity and show the varia-
tion with in Fig. 8. For h = 0, there is less variation due to
the numerous words centered around the mean happiness score
regardless of the threshold, ˛. Tuning both parameters too high
results in few sampled words and corrupts the interpretation of the
results.
Figs. 9 and 10 reveal a weakening happiness–happiness corre-
lation for users in the week networks as the path length between
nodes increases. All correlations, for each week, were significant
(p < 10−10). This suggests that the network is assortative with
“Twitter reciprocal reply networks exhibitassortativity with respect to happiness”Bliss, Kloumann, Harris, Danforth, and Dodds.Journal of Computational Science, 3, 388–397,2012. [1]
Decay in happiness correlation in social network.
Not a test of contagion …
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The very surprising tunable hedonometer:
10/01/08 01/01/09 04/01/09 07/01/09 10/01/09 01/01/10 04/01/10 07/01/10 10/01/10 01/01/11
5.4
5.6
5.8
6
6.2
6.4
6.6
6.8
7
7.2
Date
Aver
age
happin
ess
havg
∆havg : A 2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0
1 2 3 4 5 6 7 8 90
200
400
600
800
1000
1200 2∆havg
havg
Fre
quen
cy
1
B
0 1 2 30
0.5
1
1.5
2
2.5
3
∆havg
∆h
avg
C
−0.2
0
0.2
0.4
0.6
0.8
1
0 1 2 30
2000
4000
6000
8000
10000
∆havg
#w
ord
s D
0 5000 10000 15000 200000
20
40
60
80
100
word rank
%co
ver
age
F
0 1 2 30
20406080
100
∆havg
%co
ver
age
E
COcoNuTS@networksvox
Happiness
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−4 −2 0 2 4−7
−6
−5
−4
−3
−2
−1
0
imeyouno mylovelollike
a
haha
innewgood
ondonot weallup
rt
que
dontjusthavewith
hahaha
beget
greatfreehappythanksday
deandu
but nowyour
will
die
moreknowbestill
today
for
lifeshit homecant
is
wellthink
e
win
righthate music
videosee
hopelive
y
better
can
badpeople
neverconfirst
time
niceplease
thank fun
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granddaughter
confined
noose
trembling
belonged
adoring
attained
comforted
tenderness
sailed
unkind
indictment
∆havg = 1.50
Hour 13, 2010
Average happiness havg - 5
log10
norm
alize
dword
freq
uen
cyf(∆
havg)
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
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lovelikehahagoodhahahagreatfreehappythanksdie bestill lifeshit homewinhate musichopebetterbad nicethankfunlunchfriendssonggirlmoneygodsuperweekendbabyawesomewowbirthday
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diseases
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sorrow
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affectionbombing
cherishdemons gentlychampionships
slaves
satisfygrandfatherremarkable
sweetness
prosperity
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thrillwounds leisure
splendid
oceanssincerewound determination
gladlyimprovements
sweetersparkle
prisoners
discrimination qualities
troubled
moonlight
holocaustagony
disgrace
slaughter
prisoner
motherfuckers x−mas
cemeteryincorrect respectedneglected
violation
damages
drowned
viciousbruised devoted
jewels
glowingdespair
choking
violated
tortured
lonelinessdemise
killings
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freely
weep
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violations everlastingwidowcomplained
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forestsgrandchildrensorrowsobituary
unholy
betray
tumors
succeedingadored
prosecution
saddam
admirationdesirable
caress
cherishedmourns
granddaughter
adoring
comforted
tenderness
unkind
∆havg = 2.00
Hour 13, 2010
Average happiness havg - 5
log10
norm
alize
dword
freq
uen
cyf(∆
havg)
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
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lovehahahahahagreatfreehappydie illshit winhate musicfunfriendssongsuperweekendawesomebirthdayhell funnyhahahahaamazingfriendbeautifulpartyfamilysick momcutedead sweetexcitedenjoysadkill smilechristmassunbeach
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drowned
torturedkillings
weepheartache
succeeded
saddenedburial
victoriespeacefully
betray
tumors
succeedingsaddam
∆havg = 2.50
Hour 13, 2010
Average happiness havg - 5
log10
norm
alize
dword
freq
uen
cyf(∆
havg)
Online instrument: hedonometer.org
Machine: @andyreagan
Planned happiness versus tragedies.
COcoNuTS@networksvox
Happiness
Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
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The one reliable source of spontaneous,widespread happiness and despair:
Original tweet
COcoNuTS@networksvox
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Introduction
Measurement
HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
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Hedonometer
Analysis
Songs
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Measuring emotionalcontent
Hedonometer
Analysis
Songs
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SOTU
Geography
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Other Emotions
References
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Measuring emotionalcontent
Hedonometer
Analysis
Songs
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SOTU
Geography
Movement
Other Emotions
References
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Hedonometer
Analysis
Songs
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SOTU
Geography
Movement
Other Emotions
References
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HappinessSome motivation
Measuring emotionalcontent
Hedonometer
Analysis
Songs
Blogs
SOTU
Geography
Movement
Other Emotions
References
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Hedonometer
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Songs
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Geography
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Make your own:
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Quokka Labs
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Reagan et al. EPJ Data Science ( 2017) 6:28
DOI 10.1140/epjds/s13688-017-0121-9
REGULAR ART ICLE Open Access
Sentiment analysis methods forunderstanding large-scale texts: a case forusing continuum-scored words and wordshift graphsAndrew J Reagan1*, Christopher M Danforth1, Brian Tivnan2, Jake Ryland Williams3 and
Peter Sheridan Dodds1
*Correspondence:
andy@andyreagan.com1University of Vermont, 85 South
Prospect St, Burlington, VT 05405,
USA
Full list of author information is
available at the end of the article
Abstract
The emergence and global adoption of social media has rendered possible the
real-time estimation of population-scale sentiment, an extraordinary capacity which
has profound implications for our understanding of human behavior. Given the
growing assortment of sentiment-measuring instruments, it is imperative to
understand which aspects of sentiment dictionaries contribute to both their
classification accuracy and their ability to provide richer understanding of texts. Here,
we perform detailed, quantitative tests and qualitative assessments of 6
dictionary-based methods applied to 4 different corpora, and briefly examine a
further 20 methods. We show that while inappropriate for sentences,
dictionary-based methods are generally robust in their classification accuracy for
longer texts. Most importantly they can aid understanding of texts with reliable and
meaningful word shift graphs if (1) the dictionary covers a sufficiently large portion of
a given text’s lexicon when weighted by word usage frequency; and (2) words are
scored on a continuous scale.
Keywords: sentiment; sentiment analysis; sentiment dictionaries; language; natural
language processing; data visualization; text visualization
1 Introduction
As we move further into what might be called the Sociotechnocene — with increasingly
more interactions, decisions, and impact being made by globally distributed people and
algorithms — the myriad human social dynamics that have shaped our history have be-
come far more visible and measurable than ever before. Of the many ways we are now
able to characterize social systems in microscopic detail, sentiment detection for pop-
ulations at all scales has become a prominent research arena. Attempts to leverage on-
line expression for sentiment mining include prediction of stock markets [–], assessing
responses to advertising, real-time monitoring of global happiness [], and measuring a
health-related quality of life []. The diverse set of instruments produced by this work
now provide indicators that help scientists understand collective behavior, inform pub-
lic policy makers, and, in industry, gauge the sentiment of public response to marketing
© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in anymedium, pro-vided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, andindicate if changes were made.
“Sentiment analysis methods for understandinglarge-scale texts: A case for using continuum-scoredwords and word shift graphs”Reagan, Tivnan, Williams, Danforth, and Dodds.EPJ Data Science, 6, , 2017. [31]
Upshots: (1) do use wordshifts, and (2) do not use LIWC ...
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Happiness in Manhattan:
See Blog post on compstorylab.org
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The Geography of Happiness:
Mitchell et al., PLoS ONE, 2013. [29]
It’s a paper that tweets: @geographyofhapp Online Appendices Much interesting and amusing press ...
Online, interactive US map at hedonometer.org
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Online, interactive US map at hedonometer.org
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Online, interactive US map at hedonometer.org
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65 70
5.9
6
6.1
6.2
Gallup well−being
r= 0.511p= 1.50E -4
65 70
3.35
3.4
3.45r= -0. 133p= 3.64E -5
−0.5 0 0.5 162
64
66
68
70r= 0.725p= 2. 71E -9
23462
64
66
68
70r= -0. 586p= 7. 86E -6
0 5 1062
64
66
68
70
Gallu
p w
ell−
bein
g r= -0. 653p= 4. 83E -7
3.35 3.4 3.45
5.9
6
6.1
6.2
BRFSS score
r= 0. 251p= 7. 62E -2
−0.5 0 0.5 1
5.9
6
6.1
6.2
AHR score
r= 0.578p= 1. 10E -5
234
5.9
6
6.1
6.2
Peace index
r= -0. 521p= 1. 04E -4
0 5 10
5.9
6
6.1
6.2
Gun violence
Happin
ess
r= -0. 664p= 1. 99E -7
−0.5 0 0.5 1
3.35
3.4
3.45r= 0.330p= 1. 93E -2
234
3.35
3.4
3.45r= -0. 133p= 3. 59E -1
0 5 10
3.35
3.4
3.45
BR
FS
S s
core
r= -0. 305p= 3. 32E -2
−0.5 0 0.5 1
1.5
2
2.5
3
3.5
4
r= -0. 803p= 2.29E -12
0 5 10
−0.5
0
0.5
1
AH
R s
core
r= -0. 726p= 5. 30E -9
0 5 10
1.5
2
2.5
3
3.5
4
Peace index
r= 0.820p= 1. 06E -12
−12
−10
−8
−6
−4
−2
0
Note: Peace axisis reversed
Happiness: Average word happiness calculated usingLabMT 1.0 and geotagged tweets from 2011
BRFSS score: Average score from the Behavioral RiskFactor Surveillance System survey (2005−2008)
Gallup: Well−being index, based on survey data on lifeevaluation, emotional health, physical health, healthy behavior,work environment and basic access (2011)
Peace index: Composite index of Homicides per100,000 people, violent crimes per 100,000 people, Jailedpopulation per 100,000 people, Police officers per 100,000people,ease of access to small arms (2011)
AHR score: America’s Health Ranking, composite indexof Behavior, Community & Environment, Policy and Clinical Caremetrics (2011)
Gun violence: Shootings per 100,000 people (2011)
FIG. 2: Scatter plot matrix of correlations between different well-being measures. Points are colored by p-value, statistically
insignificant correlations above p = 0.01 are shown in red. Spearman’s r and p-value are reported in the inset.
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Happiest Cities:
6.12 6.13 6.14 6.15 6.16 6.17 6.18 6.19 6.2
Asheville, NC
Amarillo, TX
Porterville, CA
Boulder, CO
Nashua, NH−−MA
Lafayette, CO
Logan, UT
Gilroy, CA
Davis, CA
Santa Rosa, CA
San Clemente, CA
Simi Valley, CA
Longmont, CO
Idaho Falls, ID
Napa, CA
havg
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Saddest Cities (Sorry Beaumont):
5.88 5.89 5.9 5.91 5.92 5.93
Port Arthur, TX
Waterbury, CT
Montgomery, AL
Dalton, GA
Houma, LA
Alexandria, LA
Texarkana, TX
Lima, OH
Texas City, TX
Rapid City, SD
Flint, MI
Albany, GA
Shreveport, LA
Monroe, LA
Beaumont, TX
havg
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−10 −5 0 5 10
1
5
10
15
20
25
30
35
40
45
50
shit −↓
+↓lol
don’t −↓
gift +↑
restaurant +↑
ass −↓
bitch −↓
hate −↓
wine +↑
+↓like
damn −↓
bad −↓
hell −↓
happy +↑
christmas +↑
nice +↑
not −↓
can’t −↓
never −↓
cheers +↑
dont −↓
thank +↑
hope +↑
ain’t −↓
first +↑
lunch +↑
1st +↑
+↓me
+↓haha
you +↑
dinner +↑
+↓new
mad −↓
wrong −↓
food +↑
+↓beach
no −↓
miss −↓
beauty +↑
+↓girl
beautiful +↑
die −↓
deal +↑
hi +↑
bitches −↓
great +↑
coffee +↑
valley +↑
+↓my
−↑sad
Per word ave rage happ iness sh i f t δh av g, r (%)
Word
rankr
Tre f: Al l USA (havg=6.00)Tcomp: Napa, CA (havg=6.26)
Text s iz e :T re f T c omp
+↓ +↑
−↑ −↓
Balan ce :
−84 : +184
0 100
100
101
102
103
104
Pr
i=1δ havg, i
−10 −5 0 5 10
1
5
10
15
20
25
30
35
40
45
50
lol +↑
−↑ass
−↑shit
−↑damn
−↑gone
−↑bitch
−↑no
me +↑
−↑ain’t
−↑arrest
−↑hell
−↑don’t
like +↑
+↓haha
+↓new
−↑dont
+↓great
girl +↑
+↓park
−↑hate
−↑hurt
−↑boo
sleep +↑
+↓beach
+↓happy
−↑not
−↑stupid
money +↑
−↑bored
−↑down
+↓home
−↑pissed
+↓awesome
−↑ugh
−↑smoke
−↑mad
my +↑
funny +↑
mama +↑
−↑jail
−↑ugly
−↑nobody
+↓amazing
−↑bad
real +↑
−↑cant
+↓thanks
−↑hungry
+↓hahaha
−↑lied
Per word ave rage happ iness sh i f t δh av g, r (%)
Word
rankr
Tre f: Al l USA (havg=6.00)Tcomp: Beaumont, TX (havg=5.83)
Text s iz e :T re f T c omp
+↓ +↑
−↑ −↓
Balan ce :
−182 : +82
−100 0
100
101
102
103
104
Pr
i=1δ havg, i
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A local reaction:
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Reactions:
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Another reaction:
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Blog post: Where is the happiest city in the US?
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example. Of course, individuals observed to have a large radius couldbe tourists, or they could have a long commute. Nevertheless, we findno statistical evidence for this trend. Comparing individuals whoseaverage location falls in an area of small vs. large tweet density, weobserve little difference in their average gyradii (not shown).Moving beyond these four urban areas and looking at 472 cities in
the U.S., we do find a moderate correlation between the mean gyr-adius and city land area (Pearson r5 0.24, p5 23 1027); Fig. S3 andTable S4 show the top and bottom cities with respect to gyradii.To investigate the shape of human mobility, we normalize each
individual’s trajectory to a common reference frame (see Methods).In Fig. 3, we plot a heat map of the probability density function of thenormalized locations of all individuals. For the purposes of this dis-cussion, we will refer to deviations from an individual’s expectedlocation in the normalized reference frame as occurring in the direc-tions north, south, east, and west. Several features of the map revealinteresting patterns of movement. First, the overall west-to-east tear-drop shape of the contours demonstrates that people travel predo-minantly along their principle axis, namely heading west from theorigin along y/sy 5 0, with deviations in the orthogonal directionbecoming shorter and less frequent as they move farther away fromthe origin.Second, the appearance of two spatially distinct yellow regions
separated by a less populated green region suggests that people spendthe vast majority of their time near two locations. We refer to theselocations as the work and home locales8, where the home locale is
centered on the dark red region roughly 1 standard deviation east ofthe origin, and the work locale is centered approximately 2 standarddeviations west of the origin. These locations highlight the bimodaldistribution of principal axis corridor messages (Fig. 4A).Finally, a clear asymmetry is observed about the x/sx 5 0 axis
indicating the increasingly isotropic variation in movement sur-rounding the home locale, as compared to the work locale. We inter-pret this to be a reflection of the tendency to bemore familiar with thesurroundings of one’s home, and to explore these surroundings in amore social context (Fig. 4B). The symmetry observed when reflect-ing about the y/sy 5 0-axis is strong, demonstrating the remarkableconsistency of the movement patterns revealed by the data.In an effort to characterize the temporal and spatial structure
observed in Fig. 3, in Fig. 5 we examine locations frequently visitedby the most active members of our data set, namely the roughly 300individuals for whom we received at least 800 geolocated messages.We suspect that these individuals enabled the geolocating feature tobe on by default for all messages, as implied by the roughly O(104)geolocated messages suggested by the gardenhose rate. In Fig. 5, wefocus on these individuals specifically; of all participants, their pro-lific tweet activity most accurately reflects their movement profile.The main figure shows the probability of tweeting from each
locale, with locales ordered by rank, for each individual8. We find
that P Hað Þi
!R Hað Þi
1:3
which is approximately a Zipf distri-
bution29. This finding indicates that regardless of the number of tweet
Figure 2 | The gyradius, calculated for each individual, is shown for each tweet authored in four example cities. Tweet activity reflects population
density, with urban areas clearly visible in each city. Histograms of gyradii for each city are shown in Fig. S1, alongwith tweet locations colored by distance
from expected location (Fig. S2). The number of tweets shown for each city isN5 56650 (Chicago),N5 103,213 (Los Angeles),N5 42,089 (New York
City), and N 5 45,754 (San Francisco). Note that higher resolution versions of the four panels above can be found online28. Maps were created using
Matlab.
www.nature.com/scientificreports
SCIENTIFIC REPORTS | 3 : 2625 | DOI: 10.1038/srep02625 3
“Happiness and the Patterns of Life: AStudy of Geolocated Tweets”Frank, Mitchell, Dodds, Danforth, andDodds.Nature Scientific Reports, 3, 2625, 2013. [14]
Table 1 | Example language assessment by Mechanical Turk(labMT)27,30 words and scores. Words with neutral scores 4
word
Figure 6 | (A) Average happiness of words written as a function of distance from an author’s expected location, with tweets grouped into ten equally
populated bins. Expressed happiness grows logarithmically with distance distance from expected location. (B) A similar trend is observed when
individuals are grouped into ten equally populated bins according to their gyradius. Both trends persist through variations in binning and different
measures of mobility.
www.nature.com/
SCIENTIFIC
We grow fonder as we wander.
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Anger:word avg 𝜎war 4.16 1.01
torture 3.92 1.16murdered 3.84 1.14
motherfker 3.82 1.29anger 3.80 1.26killer 3.78 1.38fury 3.63 1.56
bombing 3.58 1.39⋮ ⋮ ⋮play 1.06 0.31idea 1.06 0.31
daughter-in-law 1.06 0.24piano 1.06 0.31stars 1.06 0.24tasty 1.04 0.20
thankful 1.02 0.14happy 1.00 0.00
Disgust:word avg 𝜎war 4.16 1.01
tortured 3.74 1.28whore 3.67 1.39
murdered 3.66 1.41asshole 3.56 1.28
killer 3.55 1.50motherfker 3.54 1.36
died 3.48 1.43holocaust 3.40 1.64⋮ ⋮ ⋮
hawaii 1.06 0.24arts 1.06 0.42joy 1.04 0.20
relaxing 1.04 0.28foundation 1.04 0.20
relax 1.04 0.20piano 1.04 0.28
presence 1.00 0.00
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Fear:word avg 𝜎war 4.20 1.02
tortured 4.18 1.39death 4.18 1.21killer 4.11 1.37
murdered 4.06 1.10jail 3.90 1.08⋮ ⋮ ⋮
banana 1.08 0.34right 1.08 0.34
properties 1.08 0.27cute 1.06 0.24topic 1.06 0.24active 1.06 0.24
wonderful 1.06 0.31dear 1.06 0.31bath 1.02 0.14
Surprise:word avg 𝜎
motherfker 3.93 1.35murdered 3.66 1.37bombing 3.52 1.49
death 3.50 1.51fatal 3.50 1.43
lottery 3.46 1.54torture 3.42 1.54
slap 3.41 1.49died 3.38 1.47
earthquake 3.32 1.54⋮ ⋮ ⋮flag 1.30 0.67
doors 1.30 0.64b/c 1.28 0.75
stuart 1.26 0.63pro 1.24 0.59
beans 1.24 0.59johnson 1.18 0.65
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angerdisgust
sadness
fear surprise
happiness
earthquake:
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grreww
wah
eek wow
ha!
earthquake:
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earthquake:
Images from Scott McCloud’s “Making Comics.” [27]
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References I
[1] C. A. Bliss, I. M. Kloumann, K. D. Harris, C. M.Danforth, and P. S. Dodds.Twitter reciprocal reply networks exhibitassortativity with respect to happiness.Journal of Computational Science, 3:388–397,2012. pdf
[2] M. M. Bradley and P. J. Lang.Affective norms for english words (anew): Stimuli,instruction manual and affective ratings.Technical report c-1, University of Florida,Gainesville, FL, 1999.
[3] H. Chang.Inventing temperature: Measurement andscientific progress.Oxford University Press, 2004.
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References II
[4] E. M. Cody, A. J. Reagan, P. S. Dodds, and C. M.Danforth.Public opinion polling with Twitter, 2016.Available online athttps://arxiv.org/abs/1608.02024. pdf
[5] T. Conner Christensen, L. Feldman Barrett,E. Bliss-Moreau, K. Lebo, and C. Kaschub.A practical guide to experience-samplingprocedures.Journal of Happiness Studies, 4:53–78, 2003.
[6] M. Csikszentmihalyi.Flow.Harper & Row, New York, 1990.
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References III
[7] M. Csikszentmihalyi, R. Larson, and S. Prescott.The ecology of adolescent activity andexperience.Journal of Youth and Adolescence, 6:281–294,1977.
[8] E. Diener and M. Y. Chan.Happy people live longer: Subjective well-beingcontributes to health and longevity.Applied Psychology: Health and Well-Being,3:1–43, 2011. pdf
[9] P. S. Dodds and C. M. Danforth.Measuring the happiness of large-scale writtenexpression: Songs, blogs, and presidents.Journal of Happiness Studies, 11(4):441–456,2009. pdf
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References IV
[10] P. S. Dodds and C. M. Danforth.Measuring the happiness of large-scale writtenexpression: songs, blogs, and presidents.Journal of Happiness Studies, 2009.doi:10.1007/s10902-009-9150-9. pdf
[11] P. S. Dodds, K. D. Harris, I. M. Kloumann, C. A.Bliss, and C. M. Danforth.Temporal patterns of happiness and informationin a global social network: Hedonometrics andTwitter.PLoS ONE, 6:e26752, 2011. pdf
[12] R. A. Easterlin.Income and happiness: towards a unified theory.The Economic Journal, 111:465–484, 2001. pdf
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References V
[13] R. A. Easterlin.Explaining happiness.Proc. Natl. Acad. Sci., 100:11176–11183, 2003.pdf
[14] M. R. Frank, L. Mitchell, P. S. Dodds, and C. M.Danforth.Happiness and the patterns of life: A study ofgeolocated Tweets.Nature Scientific Reports, 3:2625, 2013. pdf
[15] H. Gunaratana.Mindfulness in Plain English.Wisdom Publications, Somerville, Massachusetts,1992.
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[16] E. Hoffer.The Passionate State of Mind: And OtherAphorisms.Buccaneer Books, 1954.
[17] A. R. Jensen.Bias in mental testing.1980.
[18] W. T. Jones.The Classical Mind.Harcourt, Brace, Jovanovich, New York, 1970.
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References VII[20] K. Kennedy Townsend.
The Pursuit of Happiness: What the Foundersmeant—And didn’t.The Atlantic, 2011.http://www.theatlantic.com/business/archive/2011/06/the-pursuit-of-happiness-what-the-founders-meant-and-didnt/240708/.
[21] K. Kennedy Townsend.What makes life worthwhile? GDP won’t tell you.The Atlantic, 2011.http://www.theatlantic.com/business/archive/2011/06/what-makes-life-worthwhile-gdp-wont-tell-you/240343/.
[22] M. A. Killingsworth and D. T. Gilbert.A wondering mind is an unhappy mind.Science Magazine, 330:932, 2010. pdf
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[23] I. M. Kloumann, C. M. Danforth, K. D. Harris, C. A.Bliss, and P. S. Dodds.Positivity of the English language.PLoS ONE, 7:e29484, 2012. pdf
[24] R. Layard.Happiness.The Penguin Press, London, 2005.
[25] S. Lyubomirsky.The How of Happiness.The Penguin Press, New York, 2007.
[26] C. Martinelli and S. W. Parker.Deception and misreporting in a social program.forthcoming in Journal of the European EconomicAssociation, 2007. pdf
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[27] S. McCloud.Making Comics.William Morrow, 2006.
[28] G. Miller.Social Scientists wade into the Tweet stream.Science Magazine, 333:1814–1815, 2011. pdf
[29] L. Mitchell, M. R. Frank, K. D. Harris, P. S. Dodds,and C. M. Danforth.The geography of happiness: Connecting Twittersentiment and expression, demographics, andobjective characteristics of place.PLoS ONE, 8:e64417, 2013. pdf
[30] C. Osgood, G. Suci, and P. Tannenbaum.The Measurement of Meaning.University of Illinois, Urbana, IL, 1957.
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[31] A. J. Reagan, B. F. Tivnan, J. R. Williams, C. M.Danforth, and P. S. Dodds.Sentiment analysis methods for understandinglarge-scale texts: A case for usingcontinuum-scored words and word shift graphs.EPJ Data Science, 6, 2017. pdf
[32] E. Sandhaus.The New York Times Annotated Corpus.Linguistic Data Consortium, Philadelphia, 2008.
[33] D. Sobel.Longitude: The True Story of a Lone Genius WhoSolved the Greatest Scientific Problem of HisTime.Bloomsbury Publishing, US.