Human language reveals a universal positivity bias

Human language reveals a universal positivity bias

Peter Sheridan Dodds peter.dodds@uvm.edu Computational Story Lab, Vermont Advanced Computing Core, & the Department of Mathematics and Statistics, University of Vermont, Burlington, VT, 05401 Vermont Complex Systems Center, University of Vermont, Burlington, VT, 05401    Eric M. Clark Computational Story Lab, Vermont Advanced Computing Core, & the Department of Mathematics and Statistics, University of Vermont, Burlington, VT, 05401 Vermont Complex Systems Center, University of Vermont, Burlington, VT, 05401    Suma Desu Center for Computational Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139    Morgan R. Frank Computational Story Lab, Vermont Advanced Computing Core, & the Department of Mathematics and Statistics, University of Vermont, Burlington, VT, 05401 Vermont Complex Systems Center, University of Vermont, Burlington, VT, 05401    Andrew J. Reagan Computational Story Lab, Vermont Advanced Computing Core, & the Department of Mathematics and Statistics, University of Vermont, Burlington, VT, 05401 Vermont Complex Systems Center, University of Vermont, Burlington, VT, 05401    Jake Ryland Williams Computational Story Lab, Vermont Advanced Computing Core, & the Department of Mathematics and Statistics, University of Vermont, Burlington, VT, 05401 Vermont Complex Systems Center, University of Vermont, Burlington, VT, 05401    Lewis Mitchell Computational Story Lab, Vermont Advanced Computing Core, & the Department of Mathematics and Statistics, University of Vermont, Burlington, VT, 05401 Vermont Complex Systems Center, University of Vermont, Burlington, VT, 05401    Kameron Decker Harris Applied Mathematics, University of Washington, Lewis Hall #202, Box 353925, Seattle, WA, 98195.    Isabel M. Kloumann Center for Applied Mathematics, Cornell University, Ithaca, NY, 14853.    James P. Bagrow Computational Story Lab, Vermont Advanced Computing Core, & the Department of Mathematics and Statistics, University of Vermont, Burlington, VT, 05401 Vermont Complex Systems Center, University of Vermont, Burlington, VT, 05401    Karine Megerdoomian The MITRE Corporation, 7525 Colshire Drive, McLean, VA, 22102    Matthew T. McMahon The MITRE Corporation, 7525 Colshire Drive, McLean, VA, 22102    Brian F. Tivnan btivnan@mitre.org The MITRE Corporation, 7525 Colshire Drive, McLean, VA, 22102 Vermont Complex Systems Center, University of Vermont, Burlington, VT, 05401    Christopher M. Danforth chris.danforth@uvm.edu Computational Story Lab, Vermont Advanced Computing Core, & the Department of Mathematics and Statistics, University of Vermont, Burlington, VT, 05401 Vermont Complex Systems Center, University of Vermont, Burlington, VT, 05401
July 5, 2019
Abstract

Using human evaluation of 100,000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (1) the words of natural human language possess a universal positivity bias; (2) the estimated emotional content of words is consistent between languages under translation; and (3) this positivity bias is strongly independent of frequency of word usage. Alongside these general regularities, we describe inter-language variations in the emotional spectrum of languages which allow us to rank corpora. We also show how our word evaluations can be used to construct physical-like instruments for both real-time and offline measurement of the emotional content of large-scale texts.

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Human language—our great social technology—reflects that which it describes through the stories it allows to be told, and us, the tellers of those stories. While language’s shaping effect on thinking has long been controversial Whorf (1956); Chomsky (1957); Pinker (1994), we know that a rich array of metaphor encodes our conceptualizations Lakoff and Johnson (1980), word choice reflects our internal motives and immediate social roles Campbell and Pennebaker (2003); Newman (2003); Pennebaker (2011), and the way a language represents the present and future may condition economic choices Chen (2013).

In 1969, Boucher and Osgood framed the Pollyanna Hypothesis: a hypothetical, universal positivity bias in human communication Boucher and Osgood (1969). From a selection of small-scale, cross-cultural studies, they marshaled evidence that positive words are likely more prevalent, more meaningful, more diversely used, and more readily learned. However, in being far from an exhaustive, data-driven analysis of language—the approach we take here—their findings could only be regarded as suggestive. Indeed, studies of the positivity of isolated words and word stems have produced conflicting results, some pointing toward a positivity bias Bradley and Lang (1999), others the opposite Stone et al. (1966); Pennebaker et al. (2007), though attempts to adjust for usage frequency tend to recover a positivity signal Jurafsky et al. (2014).

To deeply explore the positivity of human language, we constructed 24 corpora spread across 10 languages (see Supplementary Online Material). Our global coverage of linguistically and culturally diverse languages includes English, Spanish, French, German, Brazilian Portuguese, Korean, Chinese (Simplified), Russian, Indonesian, and Arabic. The sources of our corpora are similarly broad, spanning books goo (), news outlets, social media, the web goo (2006), television and movie subtitles, and music lyrics Dodds and Payne (2009). Our work here greatly expands upon our earlier study of English alone, where we found strong evidence for a usage-invariant positivity bias Kloumann et al. (2012).

We address the social nature of language in two important ways: (1) we focus on the words people most commonly use, and (2) we measure how those same words are received by individuals. We take word usage frequency as the primary organizing measure of a word’s importance. Such a data-driven approach is crucial for both understanding the structure of language and for creating linguistic instruments for principled measurements Dodds et al. (2011); Mitchell et al. (2013). By contrast, earlier studies focusing on meaning and emotion have used ‘expert’ generated word lists, and these fail to statistically match frequency distributions of natural language Osgood et al. (1957); Stone et al. (1966); Bradley and Lang (1999); Pennebaker et al. (2007), confounding attempts to make claims about language in general. For each of our corpora we selected between 5,000 to 10,000 of the most frequently used words, choosing the exact numbers so that we obtained approximately 10,000 words for each language.

We then paid native speakers to rate how they felt in response to individual words on a 9 point scale, with 1 corresponding to most negative or saddest, 5 to neutral, and 9 to most positive or happiest Bradley and Lang (1999); Dodds et al. (2011) (see also Supplementary Online Material). This happy-sad semantic differential Osgood et al. (1957) functions as a coupling of two standard 5-point Likert scales. Participants were restricted to certain regions or countries (for example, Portuguese was rated by residents of Brazil). Overall, we collected 50 ratings per word for a total of around 5,000,000 individual human assessments, and we provide all data sets as part of the Supplementary Online Material.

Figure 1: Distributions of perceived average word happiness for 24 corpora in 10 languages. The histograms represent the 5000 most commonly used words in each corpora (see Supplementary Online Material for details), and native speakers scored words on a 1 to 9 double-Likert scale with 1 being extremely negative, 5 neutral, and 9 extremely positive. Yellow indicates positivity () and blue negativity (), and distributions are ordered by increasing median (red vertical line). The background grey lines connect deciles of adjacent distributions. Fig. S1 shows the same distributions arranged according to increasing variance.
Figure 2: Scatter plots of average happiness for words measured in different languages. We order languages from relatively most positive (Spanish) to relatively least positive (Chinese); a yellow background indicates the row language is more positive than the column language, and a blue background the converse. The overall plot matrix is symmetric about the leading diagonal, the redundancy allowing for easier comparison between languages. In each scatter plot, the key gives the number of translation-stable words for each language pair, ; the average difference in translation-stable word happiness between the row language and column language, ; and the Pearson correlation coefficient for the regression, . All -values are less than 10 Fig. S2 shows histograms of differences in average happiness for translation-stable words.
Figure 3: Examples of how word happiness varies little with usage frequency. Above each plot is a histogram of average happiness for the 5000 most frequently used words in the given corpus, matching Fig. 1. Each point locates a word by its rank and average happiness , and we show some regularly spaced example words. The descending gray curves of these jellyfish plots indicate deciles for windows of 500 words of contiguous usage rank, showing that the overall histogram’s form is roughly maintained at all scales. The ‘kkkkkk…’ words represent laughter in Brazilian Portuguese, in the manner of ‘hahaha…’. See Fig. S3 for an English translation, Figs. S4S7 for all corpora, and Figs. S8S11 for the equivalent plots for standard deviation of word happiness scores.

In Fig. 1, we show distributions of the average happiness scores for all 24 corpora, leading to our most general observation of a clear positivity bias in natural language. We indicate the above neutral part of each distribution with yellow, below neutral with blue, and order the distributions moving upwards by increasing median (vertical red line). For all corpora, the median clearly exceeds the neutral score of 5. The background gray lines connect deciles for each distribution. In Fig. S1, we provide the same distributions ordered instead by increasing variance.

As is evident from the ordering in Figs. 1 and S1, while a positivity bias is the universal rule, there are minor differences between the happiness distributions of languages. For example, Latin American-evaluated corpora (Mexican Spanish and Brazilian Portuguese) exhibit relatively high medians and, to a lesser degree, higher variances. For other languages, we see those with multiple corpora have more variable medians, and specific corpora are not ordered by median in the same way across languages (e.g., Google Books has a lower median than Twitter for Russian, but the reverse is true for German and English). In terms of emotional variance, all four English corpora are among the highest, while Chinese and Russian Google Books seem especially constrained.

We now examine how individual words themselves vary in their average happiness score between languages. Owing to the scale of our corpora, we were compelled to use an online service, choosing Google Translate. For each of the 45 language pairs, we translated isolated words from one language to the other and then back. We then found all word pairs that (1) were translationally-stable, meaning the forward and back translation returns the original word, and (2) appeared in our corpora for each language.

We provide the resulting comparison between languages at the level of individual words in Fig. 2. We use the mean of each language’s word happiness distribution derived from their merged corpora to generate a rough overall ordering, acknowledging that frequency of usage is no longer meaningful, and moreover is not relevant as we are now investigating the properties of individual words. Each cell shows a heat map comparison with word density increasing as shading moves from gray to white. The background colors reflect the ordering of each pair of languages, yellow if the row language had a higher average happiness than the column language, and blue for the reverse. In each cell, we display the number of translation-stable words between language pairs, , along with the difference in average word happiness, , where each word is equally weighted.

A linear relationship is clear for each language-language comparison, and is supported by Pearson’s correlation coefficient being in the range 0.73 to 0.89 (-value across all pairs; see Fig. 2 and Tabs. S3, S4, and S5). Overall, this strong agreement between languages, previously observed on a small scale for a Spanish-English translation Redondo et al. (2007), suggests that approximate estimates of word happiness for unscored languages could be generated with no expense from our existing data set. Some words will of course translate unsatisfactorily, with the dominant meaning changing between languages. For example ‘lying’ in English, most readily interpreted as speaking falsehoods by our participants, translates to ‘acostado’ in Spanish, meaning recumbent. Nevertheless, happiness scores obtained by translation will be serviceable for purposes where the effects of many different words are incorporated. (See the Supplementary Online Material for links to an interactive visualization of Fig. 2.)

Stepping back from examining inter-language robustness, we return to a more detailed exploration of the rich structure of each corpus’s happiness distribution. In Fig. 3, we show how average word happiness is largely independent of word usage frequency for four example corpora. We first plot usage frequency rank of the 5000 most frequently used words as a function of their average happiness score, (background dots), along with some example evenly-spaced words. (We note that words at the extremes of the happiness scale are ones evaluators agreed upon strongly, while words near neutral range from being clearly neutral (e.g., =4.98) to contentious with high standard deviation Kloumann et al. (2012).) We then compute deciles for contiguous sets of 500 words, sliding this window through rank . These deciles form the vertical strands. We overlay randomly chosen, equally-spaced example words to give a sense of each corpus’s emotional texture.

We chose the four example corpora shown in Fig. 3 to be disparate in nature, covering diverse languages (French, Egyptian Arabic, Brazilian Portuguese, and Chinese), regions of the world (Europe, the Middle East, South America, and Asia), and texts (Twitter, movies and television, the Web goo (2006), and books goo ()). In the Supplementary Online Material, we show all 24 corpora yield similar plots (see Figs. S4S7 and English translated versions, Figs. S12S15). We also show how the standard deviation for word happiness exhibits an approximate self-similarity (Figs. S8S11 and their translations, Figs. S16S19).

Across all corpora, we observe visually that the deciles tend to stay fixed or move slightly toward the negative, with some expected fragility at the 10% and 90% levels (due to the distributions’ tails), indicating that each corpus’s overall happiness distribution approximately holds independent of word usage. In Fig. 3, for example, we see that both the Brazilian Portuguese and French examples show a small shift to the negative for increasingly rare words, while there is no visually clear trend for the Arabic and Chinese cases. Fitting typically returns on the order of -1 suggesting decreases 0.1 per 10,000 words. For standard deviations of happiness scores (Figs. S8S11), we find a similarly weak drift toward higher values for increasingly rare words (see Tabs. S6 and S7 for correlations and linear fits for and as a function of word rank for all corpora). We thus find that, to first order, not just the positivity bias, but the happiness distribution itself applies for common words and rare words alike, revealing an unexpected addition to the many well known scalings found in natural language, famously exemplified by Zipf’s law Zipf (1949).

Figure 4: Emotional time series for three great 19th century works of literature: Melville’s Moby Dick, Dostoyevsky’s Crime and Punishment, and Dumas’ Count of Monte Cristo. Each point represents the language-specific happiness score for a window of 10,000 words (converted to lowercase), with the window translated throughout the work. The overlaid word shifts show example comparisons between different sections of each work. Word shifts indicate which words contribute the most toward and against the change in average happiness between two texts (see pp. Explanation of Word ShiftsFull Word Shifts). While a robust instrument in general, we acknowledge the hedonometer’s potential failure for individual words both due to language evolution and words possessing more than one meaning. While a robust instrument in general, we acknowledge the hedonometer’s potential failure for individual words both due to language evolution and words possessing more than one meaning. For Moby Dick, we excluded ‘cried’ and ‘cry’ (to speak loudly rather than weep) and ‘Coffin’ (surname, still common on Nantucket). Such alterations, which can be done on a case by case basis, do not noticeably change the overall happiness curves while leaving the word shifts more informative. We provide links to online, interactive versions of these time series in the Supplementary Online Information.

In constructing language-based instruments for measuring expressed happiness, such as our hedonometer Dodds et al. (2011), this frequency independence allows for a way to ‘increase the gain’ in a way resembling that of standard physical instruments. Moreover, we have earlier demonstrated the robustness of our hedonometer for the English language, showing, for example that measurements derived from Twitter correlate strongly with Gallup well-being polls and related indices at the state and city level for the United States Mitchell et al. (2013).

Here, we provide an illustrative use of our hedonometer in the realm of literature, inspired by Vonnegut’s shapes of stories Vonnegut (2005); von (). In Fig. 4, we show ‘happiness time series’ for three famous works of literature, evaluated in their original languages English, Russian, and French: A. Melville’s Moby Dick gut (), B. Dostoyevsky’s Crime and Punishment Dostoyevsky (), and C. Dumas’ Count of Monte Cristo gut (). We slide a 10,000-word window through each work, computing the average happiness using a ‘lens’ for the hedonometer in the following manner. We capitalize on our instrument’s tunablility to obtain a strong signal by excluding all words for which , i.e., we keep words residing in the tails of each distribution Dodds et al. (2011). Denoting a given lens by its corresponding set of allowed words , we estimate the happiness score of any text as where is the frequency of word in  Dodds and Danforth (2009).

The three resulting happiness time series provide interesting, detailed views of each work’s narrative trajectory revealing numerous peaks and troughs throughout, at times clearly dropping below neutral. Both Moby Dick and Crime and Punishment end on low notes, whereas the Count of Monte Cristo culminates with a rise in positivity, accurately reflecting the finishing arcs of all three. The ‘word shifts’ overlaying the time series compare two distinct regions of each work, showing how changes in word abundances lead to overall shifts in average happiness. Such word shifts are essential tests of any sentiment measurement, and are made possible by the linear form of our instrument Dodds and Danforth (2009); Dodds et al. (2011) (see pp. Explanation of Word ShiftsFull Word Shifts in the Supplementary Online Material for a full explanation). As one example, the third word shift for Moby Dick shows why the average happiness of the last 10% of the book is well below that of the first 25%. The major contribution is an increase in relatively negative words including ‘missing’, ‘shot’, ‘poor’, ‘die’, and ‘evil’. We include full diagnostic versions of all word shifts in Figs. S21S34.

By adjusting the lens, many other related time series can be formed such as those produced by focusing on only positive or negative words. Emotional variance as a function of text position can also be readily extracted. In the Supplementary Online Material, we provide links to online, interactive versions of these graphs where different lenses and regions of comparisons may be easily explored. Beyond this example tool we have created here for the digital humanities and our hedonometer for measuring population well-being, the data sets we have generated for the present study may be useful in creating a great variety of language-based instruments for assessing emotional expression.

Overall, our major scientific finding is that when experienced in isolation and weighted properly according to usage, words—the atoms of human language—present an emotional spectrum with a universal, self-similar positive bias. We emphasize that this apparent linguistic encoding of our social nature is a system level property, and in no way asserts all natural texts will skew positive (as exemplified by certain passages of the three works in Fig. 4), or diminishes the salience of negative states Forgas (2013). Nevertheless, a general positive bias points towards a positive social evolution, and may be linked to the gradual if haphazard trajectory of modern civilization toward greater human rights and decreases in violence Pinker (2011). Going forward, our word happiness assessments should be periodically repeated, and carried out for new languages, tested on different demographics, and expanded to phrases, both for the improvement of hedonometric instruments and to chart the dynamics of our collective social self.

References

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The authors acknowledge I. Ramiscal, C. Burke, P. Carrigan, M. Koehler, and Z. Henscheid, in part for their roles in developing hedonometer.org. The authors are also grateful for conversations with F. Henegan, A. Powers, and N. Atkins. PSD was supported by NSF CAREER Award # 0846668.

Supplementary Online Material

Online, interactive visualizations:

Spatiotemporal hedonometric measurements of Twitter across all 10 languages can be explored at hedonometer.org.

We provide the following resources online at http://www.uvm.edu/~storylab/share/papers/dodds2014a/.

  • Example scripts for parsing and measuring average happiness scores for texts;

  • D3 and Matlab scripts for generating word shifts;

  • Visualizations for exploring translation-stable word pairs across languages;

  • Interactive time series for Moby Dick, Crime and Punishment, the Count of Monte Cristo, and other works of literature.

Corpus: # Words Reference(s)
English: Twitter 5000 twi (); Dodds et al. (2011)
English: Google Books Project 5000 goo (); Michel et al. (2011)
English: The New York Times 5000 Sandhaus (2008)
English: Music lyrics 5000 Dodds and Danforth (2009)
Portuguese: Google Web Crawl 7133 goo (2006)
Portuguese: Twitter 7119 twi ()
Spanish: Google Web Crawl 7189 goo (2006)
Spanish: Twitter 6415 twi ()
Spanish: Google Books Project 6379 goo (); Michel et al. (2011)
French: Google Web Crawl 7056 goo (2006)
French: Twitter 6569 twi ()
French: Google Books Project 6192 goo (); Michel et al. (2011)
Arabic: Movie and TV subtitles 9999 The MITRE Corporation
Indonesian: Twitter 7044 twi ()
Indonesian: Movie subtitles 6726 The MITRE Corporation
Russian: Twitter 6575 twi ()
Russian: Google Books Project 5980 goo (); Michel et al. (2011)
Russian: Movie and TV subtitles 6186 goo (2006)
German: Google Web Crawl 6902 goo (2006)
German: Twitter 6459 twi ()
German: Google Books Project 6097 goo (); Michel et al. (2011)
Korean: Twitter 6728 twi ()
Korean: Movie subtitles 5389 The MITRE Corporation
Chinese: Google Books Project 10000 goo (); Michel et al. (2011)
Table S1: Sources for all corpora.
English United States of America, India
German Germany
Indonesian Indonesia
Russian Russia
Arabic Egypt
French France
Spanish Mexico
Portuguese Brazil
Simplified Chinese China
Korean Korea, United States of America
Table S2: Main country of location for participants.

Corpora

We used the services of Appen Butler Hill (http://www.appen.com) for all word evaluations excluding English, for which we had earlier employed Mechanical Turk (https://www.mturk.com/ Kloumann et al. (2012)).

English instructions were translated to all other languages and given to participants along with survey questions, and an example of the English instruction page is below. Non-english language experiments were conducted through a custom interactive website built by Appen Butler Hill, and all participants were required to pass a stringent oral proficiency test in their own language.

{myframe}

Sizes and sources for our 24 corpora are given in Tab. S1.

We used Mechanical Turk to obtain evaluations of the four English corpora Kloumann et al. (2012). For all non-English assessments, we contracted the translation services company Appen-Butler Hill. For each language, participants were required to be native speaker, to have grown up in the country where the language is spoken, and to pass a strenuous online aural comprehension test.

Notes on corpus generation

There is no single, principled way to merge corpora to create an ordered list of words for a given language. For example, it is impossible to weight the most commonly used words in the New York Times against those of Twitter. Nevertheless, we are obliged to choose some method for doing so to facilitate comparisons across languages and for the purposes of building adaptable linguistic instruments.

For each language, we created a single quasi-ranked word list by finding the smallest integer such that the union of all words with rank in at least one corpus formed a set of at least 10,000 words.

For Twitter, we first checked if a string contains at least one valid utf8 letter, discarding if not. Next we filtered out strings containing invisible control characters, as these symbols can be problematic. We ignored all strings that start with and end with (generally html code). We ignored strings with a leading @ or &, or either preceded with standard punctuation (e.g., Twitter ID’s), but kept hashtags. We also removed all strings starting with www. or http: or end in .com (all websites). We stripped the remaining strings of standard punctuation, and we replaced all double quotes (”) by single quotes (’). Finally, we converted all Latin alphabet letters to lowercase.

A simple example of this tokenization process would be:

Term count
love 10
LoVE 5
love! 2
#love 3
.love 2
@love 1
love87 1

Term count love 19 #love 3 love87 1

The term ‘@love’ is discarded, and all other terms map to either ‘love’ or ‘love87’.

Figure S1: The same average happiness distributions shown in Fig. 1 re-ordered by increasing variance. Yellow indicates above neutral (), blue below neutral, red vertical lines mark each distribution’s median, and the gray background lines connect the deciles of adjacent distributions.
Spanish Portuguese English Indonesian French German Arabic Russian Korean Chinese
Spanish 1.00, 0.00 1.01, 0.03 1.06, -0.07 1.22, -0.88 1.11, -0.24 1.22, -0.84 1.13, -0.22 1.31, -1.16 1.60, -2.73 1.58, -2.30
Portuguese 0.99, -0.03 1.00, 0.00 1.04, -0.03 1.22, -0.97 1.11, -0.33 1.21, -0.86 1.09, -0.08 1.26, -0.95 1.62, -2.92 1.58, -2.39
English 0.94, 0.06 0.96, 0.03 1.00, 0.00 1.13, -0.66 1.06, -0.23 1.16, -0.75 1.05, -0.10 1.21, -0.91 1.51, -2.53 1.47, -2.10
Indonesian 0.82, 0.72 0.82, 0.80 0.88, 0.58 1.00, 0.00 0.92, 0.48 0.99, 0.06 0.89, 0.71 1.02, 0.04 1.31, -1.53 1.33, -1.42
French 0.90, 0.22 0.90, 0.30 0.94, 0.22 1.09, -0.52 1.00, 0.00 1.08, -0.44 0.99, 0.12 1.12, -0.50 1.37, -1.88 1.40, -1.77
German 0.82, 0.69 0.83, 0.71 0.86, 0.65 1.01, -0.06 0.92, 0.41 1.00, 0.00 0.91, 0.61 1.07, -0.25 1.29, -1.44 1.32, -1.36
Arabic 0.88, 0.19 0.92, 0.08 0.95, 0.10 1.12, -0.80 1.01, -0.12 1.10, -0.68 1.00, 0.00 1.12, -0.63 1.40, -2.14 1.43, -2.01
Russian 0.76, 0.88 0.80, 0.75 0.83, 0.75 0.98, -0.04 0.89, 0.45 0.93, 0.24 0.89, 0.56 1.00, 0.00 1.26, -1.39 1.25, -1.05
Korean 0.62, 1.70 0.62, 1.81 0.66, 1.67 0.77, 1.17 0.73, 1.37 0.78, 1.12 0.71, 1.53 0.79, 1.10 1.00, 0.00 0.98, 0.28
Chinese 0.63, 1.46 0.63, 1.51 0.68, 1.43 0.75, 1.07 0.71, 1.26 0.76, 1.03 0.70, 1.41 0.80, 0.84 1.02, -0.29 1.00, 0.00
Table S3: Reduced Major Axis (RMA) regression fits for row language as a linear function of the column language: where indicates a translation-stable word. Each entry in the table contains the coefficient pair and . See the scatter plot tableau of Fig. 2 for further details on all language-language comparisons. We use RMA regression, also known as Standardized Major Axis linear regression, because of its accommodation of errors in both variables.
Spanish Portuguese English Indonesian French German Arabic Russian Korean Chinese
Spanish 1.00 0.89 0.87 0.82 0.86 0.82 0.83 0.73 0.79 0.79
Portuguese 0.89 1.00 0.87 0.82 0.84 0.81 0.84 0.84 0.79 0.76
English 0.87 0.87 1.00 0.88 0.86 0.82 0.86 0.87 0.82 0.81
Indonesian 0.82 0.82 0.88 1.00 0.79 0.77 0.83 0.85 0.79 0.77
French 0.86 0.84 0.86 0.79 1.00 0.84 0.77 0.84 0.79 0.76
German 0.82 0.81 0.82 0.77 0.84 1.00 0.76 0.80 0.73 0.74
Arabic 0.83 0.84 0.86 0.83 0.77 0.76 1.00 0.83 0.79 0.80
Russian 0.73 0.84 0.87 0.85 0.84 0.80 0.83 1.00 0.80 0.82
Korean 0.79 0.79 0.82 0.79 0.79 0.73 0.79 0.80 1.00 0.81
Chinese 0.79 0.76 0.81 0.77 0.76 0.74 0.80 0.82 0.81 1.00
Table S4: Pearson correlation coefficients for translation-stable words for all language pairs. All -values are . These values are included in Fig. 2 and reproduced here for to facilitate comparison.
Spanish Portuguese English Indonesian French German Arabic Russian Korean Chinese
Spanish 1.00 0.85 0.83 0.77 0.81 0.77 0.75 0.74 0.74 0.68
Portuguese 0.85 1.00 0.83 0.77 0.78 0.77 0.77 0.81 0.75 0.66
English 0.83 0.83 1.00 0.82 0.80 0.78 0.78 0.81 0.75 0.70
Indonesian 0.77 0.77 0.82 1.00 0.72 0.72 0.76 0.77 0.71 0.71
French 0.81 0.78 0.80 0.72 1.00 0.80 0.67 0.79 0.71 0.64
German 0.77 0.77 0.78 0.72 0.80 1.00 0.69 0.76 0.64 0.62
Arabic 0.75 0.77 0.78 0.76 0.67 0.69 1.00 0.74 0.69 0.68
Russian 0.74 0.81 0.81 0.77 0.79 0.76 0.74 1.00 0.70 0.66
Korean 0.74 0.75 0.75 0.71 0.71 0.64 0.69 0.70 1.00 0.71
Chinese 0.68 0.66 0.70 0.71 0.64 0.62 0.68 0.66 0.71 1.00
Table S5: Spearman correlation coefficients for translation-stable words. All -values are .
Figure S2: Histograms of the change in average happiness for translation-stable words between each language pair, companion to Fig. 2 given in the main text. The largest deviations correspond to strong changes in a word’s perceived primary meaning (e.g., ‘lying’ and ‘acostado’). As per Fig. 2, the inset quantities are , the number of translation-stable words, and is the average difference in translation-stable word happiness between the row language and column language.
Language: Corpus -value -value
Spanish: Google Web Crawl -0.114 3.38 -0.090 1.85 -5.55 6.10
Spanish: Google Books -0.040 1.51 -0.016 1.90 -2.28 5.90
Spanish: Twitter -0.048 1.14 -0.032 1.10 -3.10 5.94
Portuguese: Google Web Crawl -0.085 6.33 -0.060 3.23 -3.98 5.96
Portuguese: Twitter -0.041 5.98 -0.030 1.15 -2.40 5.73
English: Google Books -0.042 3.03 -0.013 3.50 -3.04 5.62
English: New York Times -0.056 6.93 -0.044 1.99 -4.17 5.61
German: Google Web Crawl -0.096 1.11 -0.082 6.75 -3.67 5.65
French: Google Web Crawl -0.105 9.20 -0.080 1.99 -4.50 5.68
English: Twitter -0.097 6.56 -0.103 2.37 -7.78 5.67
Indonesian: Movie subtitles -0.039 1.48 -0.063 2.45 -2.04 5.45
German: Twitter -0.054 1.47 -0.036 4.02 -2.51 5.58
Russian: Twitter -0.052 2.38 -0.028 2.42 -2.55 5.52
French: Google Books -0.043 6.80 -0.030 1.71 -2.31 5.49
German: Google Books -0.003 8.12 +0.014 2.74 -1.38 5.45
French: Twitter -0.049 6.08 -0.023 6.31 -2.54 5.54
Russian: Movie and TV subtitles -0.029 2.36 -0.033 9.17 -1.57 5.43
Arabic: Movie and TV subtitles -0.045 7.10 -0.029 4.19 -1.66 5.44
Indonesian: Twitter -0.051 2.14 -0.018 1.24 -2.50 5.46
Korean: Twitter -0.032 8.29 -0.016 1.91 -1.24 5.38
Russian: Google Books +0.030 2.09 +0.070 5.08 +1.20 5.35
English: Music Lyrics -0.073 2.53 -0.081 1.05 -6.12 5.45
Korean: Movie subtitles -0.187 8.22 -0.180 2.01 -9.66 5.41
Chinese: Google Books -0.067 1.48 -0.050 5.01 -1.72 5.21
Table S6: Pearson correlation coefficients and -values, Spearman correlation coefficients and -values, and linear fit coefficients, for average word happiness as a function of word usage frequency rank . We use the fit is for the most common 5000 words in each corpora, determining and via ordinary least squares, and order languages by the median of their average word happiness scores (descending). We note that stemming of words may affect these estimates.
Language: Corpus -value -value
Portuguese: Twitter +0.090 2.55 +0.095 1.28 1.19 1.29
Spanish: Twitter +0.097 8.45 +0.104 5.92 1.47 1.26
English: Music Lyrics +0.129 4.87 +0.134 1.63 2.76 1.33
English: Twitter +0.007 6.26 +0.012 4.11 1.47 1.35
English: New York Times +0.050 4.56 +0.044 1.91 9.34 1.32
Arabic: Movie and TV subtitles +0.101 7.13 +0.101 3.41 9.41 1.01
English: Google Books +0.180 1.68 +0.176 4.96 3.36 1.27
Spanish: Google Books +0.066 1.23 +0.062 6.53 9.17 1.26
Indonesian: Movie subtitles +0.026 3.43 +0.027 2.81 2.87 1.12
Russian: Movie and TV subtitles +0.083 7.60 +0.075 3.28 1.06 0.89
French: Twitter +0.072 4.77 +0.076 8.94 1.07 1.05
Indonesian: Twitter +0.072 1.17 +0.072 1.73 8.16 1.12
French: Google Books +0.090 1.02 +0.085 1.67 1.25 1.02
Russian: Twitter +0.055 6.83 +0.053 1.67 7.39 0.91
Spanish: Google Web Crawl +0.119 4.45 +0.106 2.60 1.45 1.23
Portuguese: Google Web Crawl +0.093 4.06 +0.083 2.91 1.07 1.26
German: Twitter +0.051 4.45 +0.050 5.15 7.39 1.15
French: Google Web Crawl +0.104 2.12 +0.088 9.64 1.27 1.01
Korean: Movie subtitles +0.171 1.39 +0.185 8.85 2.58 0.88
German: Google Books +0.157 6.06 +0.162 4.96 2.17 1.03
Korean: Twitter +0.056 4.07 +0.062 4.25 6.98 0.93
German: Google Web Crawl +0.099 2.05 +0.085 1.18 1.20 1.07
Chinese: Google Books +0.099 3.07 +0.097 3.81 8.70 1.16
Russian: Google Books +0.187 5.15 +0.177 2.24 2.28 0.81
Table S7: Pearson correlation coefficients and -values, Spearman correlation coefficients and -values, and linear fit coefficients for standard deviation of word happiness as a function of word usage frequency rank . We consider the fit is for the most common 5000 words in each corpora, determining and via ordinary least squares, and order corpora according to their emotional variance (descending).
Figure S3: Reproduction of Fig. 3 in the main text with words directly translated into English using Google Translate. See the caption of Fig. 3 for details.
Figure S4: Jellyfish plots showing how average word happiness distribution is strongly invariant with respect to word rank for corpora ranked 1–6 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S5: Jellyfish plots showing how average word happiness distribution is strongly invariant with respect to word rank for corpora ranked 7–12 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S6: Jellyfish plots showing how average word happiness distribution is strongly invariant with respect to word rank for corpora ranked 13–18 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S7: Jellyfish plots showing how average word happiness distribution is strongly invariant with respect to word rank for corpora ranked 19–24 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S8: Jellyfish plots showing how standard deviation of word happiness behaves with respect to word rank for corpora ranked 1–6 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S9: Jellyfish plots showing how standard deviation of word happiness behaves with respect to word rank for corpora ranked 7–12 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S10: Jellyfish plots showing how standard deviation of word happiness behaves with respect to word rank for corpora ranked 13–18 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S11: Jellyfish plots showing how standard deviation of word happiness behaves with respect to word rank for corpora ranked 19–24 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S12: English-translated Jellyfish plots showing how average word happiness distribution is strongly invariant with respect to word rank for corpora ranked 1–6 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S13: English-translated Jellyfish plots showing how average word happiness distribution is strongly invariant with respect to word rank for corpora ranked 7–12 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S14: English-translated Jellyfish plots showing how average word happiness distribution is strongly invariant with respect to word rank for corpora ranked 13–18 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S15: English-translated Jellyfish plots showing how average word happiness distribution is strongly invariant with respect to word rank for corpora ranked 19–24 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S16: English-translated Jellyfish plots showing how standard deviation of word happiness behaves with respect to word rank for corpora ranked 1–6 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S17: English-translated Jellyfish plots showing how standard deviation of word happiness behaves with respect to word rank for corpora ranked 7–12 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S18: English-translated Jellyfish plots showing how standard deviation of word happiness behaves with respect to word rank for corpora ranked 13–18 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S19: English-translated Jellyfish plots showing how standard deviation of word happiness behaves with respect to word rank for corpora ranked 19–24 according to median word happiness. See the caption of Fig. 3 in the main text for details.
Figure S20: Fig. 4 from the main text with Russian and French translated into English.

Explanation of Word Shifts

In this section, we explain our word shifts in detail, both the abbreviated ones included in Figs. 4 and S20, and the more sophisticated, complementary word shifts which follow in this supplementary section. We expand upon the approach described in Dodds and Danforth (2009) and Dodds et al. (2011) to rank and visualize how words contribute to this overall upward shift in happiness.

Shown below is the third inset word shift used in Fig 4 for the Count of Monte Cristo, a comparison of words found in the last 10% of the book (, = 6.32) relative to those used between 30% and 40% (, = 4.82). For this particular measurement, we employed the ‘word lens’ which excluded words with .

{myframe}

We will use the following probability notation for the normalized frequency of a given word in a text :

(1)

where is the frequency of word in with word lens applied Dodds and Danforth (2009). (For the example word shift above, we have .) We then estimate the happiness score of any text as

(2)

where is the average happiness score of a word as determined by our survey.

We can now express the happiness difference between two texts as follows:

where we have introduced as base reference for the average happiness of a word by noting that

(4)

We can now see the change in average happiness between a reference and comparison text as depending on how these two quantities behave for each word:

(5)

and

(6)

Words can contribute to or work against a shift in average happiness in four possible ways which we encode with symbols and colors:

  • , : Words that are more positive than the reference text’s overall average and are used more in the comparison text (, strong yellow).

  • , : Words that are less positive than the reference text’s overall average but are used less in the comparison text (, pale blue).

  • , : Words that are more positive than the reference text’s overall average but are used less in the comparison text (, pale yellow).

  • , : Words that are more positive than the reference text’s overall average and are used more in the comparison text (, strong blue).

Regardless of usage changes, yellow indicates a relatively positive word, blue a negative one. The stronger colors indicate words with the most simple impact: relatively positive or negative words being used more overall.

We order words by the absolute value of their contribution to or against the overall shift, and normalize them as percentages.

Simple Word Shifts

For simple inset word shifts, we show the 10 top words in terms of their absolute contribution to the shift.

Returning to the inset word shift above, we see that an increase in the abundance of relatively positive words ‘excellence’ ‘mer’ and ‘rêve’ (, strong yellow) as well as a decrease in the relatively negative words ‘prison’ and ‘prisonnier’ (, pale blue) most strongly contribute to the increase in positivity. Some words go against this trend, and in the abbreviated word shift we see less usage of relatively positive words ‘liberté’ and ‘été’ (, pale yellow).

The normalized sum total of each of the four categories of words is shown in the summary bars at the bottom of the word shift. For example, represents the total shift due to all relatively positive words that are more prevalent in the comparison text. The smallest contribution comes from relatively negative words being used more (, strong blue).

The bottom bar with shows the overall shift with a breakdown of how relatively positive and negative words separately contribute. For the Count of Monte Cristo example, we observe an overall use of relatively positive words and a drop in the use of relatively negative ones (strong yellow and pale blue).

Full Word Shifts

We turn now to explaining the sophisticated word shifts we include at the end of this document. We break down the full word shift corresponding to the simple one we have just addressed for the Count of Monte Cristo, Fig. S34.

First, each word shift has a summary at the top:

{myframe}

which describes both the reference and summary text, gives their average happiness scores, shows which is happier through an inequality, and functions as a legend showing that average happiness will be marked on graphs with diamonds (filled for the reference text, unfilled for the comparison one).

We note that if two texts are equal in happiness two two decimal places, the word shift will show them as approximately the same. The word shift is still very much informative as word usage will most likely have be different between any two large-scale texts.

Below the summary and taking up the left column of each figure, is the word shift itself for the first 50 words, ordered by contribution rank:

{myframe}

⋮ ⋮ ⋮


⋮ ⋮ ⋮ 

The right column of each figure contains a series of summary and histogram graphics that show how the underlying word distributions for each text give rise to the overall shift. In all cases, and in the manner of the word shift, data for the reference text is on the left, the comparison is on the right. In the histograms, we indicate the lens with a pale red for inclusion, light gray for exclusion. We mark average happiness for each text by black and unfilled diamonds.

First in plot B, we have the bare frequency distributions for each text. The left hand summary compares the sizes of the two texts (the reference is larger in this case), while the histogram gives a detailed view of how each text’s words are distributed according to average happiness.

{myframe}

In plot C, we then apply the lens and renormalize. We can now also use our colors to show the relative positivity or negativity of words. Note that the strong yellow and blue appear on the side of comparison text, as these words are being used more relative to the reference text, and we are still considering normalized word counts only. The plot on the left shows the sum of the four kinds of counts. We can see that relatively positive words are dominating in terms of pure counts at this stage of the computation.

{myframe}

We move to plot D, where we weight words by their emotional distance from the reference text, . We note that in this particular example, the reference text’s average happiness is near neutral ( = 5), so the shapes of histograms do not change greatly. Also, since is negative, the colors for the relatively negative words swap from left to right. More frequently used negative words, for example, drag the comparison text down (strong blue) and must switch toward favoring the reference text.

{myframe}

In plot E, we incorporate the differences in word usage, . The histogram shows the result binned by average happiness, and in this case we see that the comparison text is generally happier across the negativity-positivity scale. The summary plot shows both the sums of relatively positive and negative words, and the overall differential. These three bars match those at the bottom of the corresponding simple word shift.

{myframe}

Finally, we show how the four categories of words combine as we sum their contributions up in descending order of absolute contribution to or against the overall happiness shift. The four outer plots below show the growth for each kind of word separately, and their end points match the bar lengths in Plot D above. The central plot shows how all four contribute together with the black line showing the overall sum. In this example, the shift is positive, and all the sum of all contributions gives +100%. The horizontal line in all five plots indicates a word rank of 50, to match the extent of Figure’s word shift.

{myframe}

In the remaining pages of this document, we provide full word shifts matching the simple ones included in Figs. 4 and S20.

Figure S21: Detailed version of the first word shift for Moby Dick in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
Figure S22: Detailed version of the second word shift for Moby Dick in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
Figure S23: Detailed version of the third word shift for Moby Dick in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
Figure S24: Detailed version of the first word shift for Crime and Punishment in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
Figure S25: Detailed English translation version of the first word shift for Crime and Punishment in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
Figure S26: Detailed version of the second word shift for Crime and Punishment in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
Figure S27: Detailed English translation version of the second word shift for Crime and Punishment in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
Figure S28: Detailed version of the third word shift for Crime and Punishment in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
Figure S29: Detailed English translation version of the third word shift for Crime and Punishment in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
Figure S30: Detailed version of the first word shift for the Count of Monte Cristo in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
Figure S31: Detailed English translation version of the first word shift for the Count of Monte Cristo in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
Figure S32: Detailed version of the second word shift for the Count of Monte Cristo in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
Figure S33: Detailed English translation version of the second word shift for the Count of Monte Cristo in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
Figure S34: Detailed version of the third word shift for the Count of Monte Cristo in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
Figure S35: Detailed English translation version of the third word shift for the Count of Monte Cristo in Fig. 4. See pp. Explanation of Word ShiftsFull Word Shifts for a full explanation.
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