Abstract We report here trends in the usage of “mood” words, that is, words carrying emotional content, in 20th century English language books, using the data set provided by Google that includes word frequencies in roughly 4% of all books published up to the year 2008. We find evidence for distinct historical periods of positive and negative moods, underlain by a general decrease in the use of emotion-related words through time. Finally, we show that, in books, American English has become decidedly more “emotional” than British English in the last half-century, as a part of a more general increase of the stylistic divergence between the two variants of English language.

Citation: Acerbi A, Lampos V, Garnett P, Bentley RA (2013) The Expression of Emotions in 20th Century Books. PLoS ONE 8(3): e59030. https://doi.org/10.1371/journal.pone.0059030 Editor: Sune Lehmann, Technical University of Denmark, Denmark Received: July 26, 2012; Accepted: February 11, 2013; Published: March 20, 2013 Copyright: © 2013 Acerbi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This research was partially supported by the Leverhulme Trust "Tipping Points" program. A. Acerbi was initially supported by the "Uniquely Human" project funded by the Swedish Research Council, and then by a Newton International Fellowship. V. Lampos acknowledges the support from the TrendMiner project (EU-FP7-ICT n.287863). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: Co-author R. Alexander Bentley is a PLOS ONE Editorial Board member. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

Introduction Studies of cultural change and evolution have been transformed recently by a new level of accessibility and volume of electronic data concerning human behavior [1]. Among these, new studies of word usage can access mass everyday public preferences that may be missed in mainstream history and politics [2]–[8]. For example, interest in mass public ‘mood’ was sparked by claims that Twitter mood words can predict stock market trends [9], and, in general, sentiment analysis of Twitter stream is a growing field of research [10]–[12]. This has been paired by findings of surprisingly straightforward patterns about word usage online, such as S-curves of adoption [13]–[15], or traders whose instant messaging patterns correlate with their financial success [16]–[18]. While these studies focus on the recent, short-time scale of online media, one of the exciting challenges of the ‘Big Data’ agenda is to address cultural dynamics at longer time scales. Language itself is a remarkably long-lived phenomenon, with most of the common words in a language having been passed down through multiple generations for centuries or millennia [3], [4], [19]. While studies of word usage in different online communities [13] can achieve unprecedented sample sizes, long-term temporal dynamics and diversity are exhibited by the history and inferred prehistory of human languages [20]–[23]. In a novel approach, Hughes et al. [7] investigated past centuries of books (using the Project Gutenberg Digital Library) through the frequencies of ‘content-free’ words, that is words that carry little or not meaning on their own, but acquire it according to the context in which they are used, such as “to be” verbs, conjunctions (e.g. and, but), articles (e.g. the), pronouns (e.g. you, us) and prepositions (e.g. about, within). Representing authors by a vector of normalised content-free word frequencies used in their works, Hughes et al. [7] found temporal regimes of similarity among authors (1784–1829, 1825–1870, 1866–1911, and 1907–1952) that provided a fascinating comparison with well-known historical genres of literature. In evolutionary terms, words with specific content are selected to serve a more direct purpose, but the content-free words subject to random drift mark differences in stylistic genre. If content-free words are a good proxy for stylistic change, we may add in increments of ‘content’ and see how patterns change. For example, using Google's Ngram database [2], Twenge et al. [8] found that the summed frequencies of ‘individualistic’ words (e.g., independent, individual, unique, self, solitary, personal) significantly increased in American books between 1960 and 2008, while ‘communal’ words (e.g., communal, team, collective, village, group, union) did not. During those same 48 years, individualistic phrases (e.g., all about me, I get what I want) also increased in frequency compared to communal phrases (e.g., band together, unitedi we stand) [8]. Here we analyze trends in the past century of mood words in books, using Google's Ngram database. Google's Ngram database represents a 4% digitally–scanned sample of several centuries of books, for a total of 5,195,769 volumes [2]. The corpus contains texts in different languages, and, for English, a further distinctions is made between American English and British English (according to the country of publication, i.e. United States versus Great Britain). Additionally, a subset of English texts collects only fiction books. Titles of books present in the corpus are not available because of copyright reasons [2]. The corpus gives information on how many times, in a given year, an 1-gram or an n-gram is used, where an 1-gram is a string of characters uninterrupted by space (i.e. a word, but also numbers, typos, etc.) and an n-gram is a sequence of n 1-grams. We make use of six unique lists of terms (see Methods) to characterize mood categories labeled as Anger, Disgust, Fear, Joy, Sadness, and Surprise. These mood word lists have previously been applied on a study of U.K. Twitter content, which showed that changes in these mood word frequencies identified real-world events such as the unexpected deaths of popular personas, public unrest, or natural disasters [24]. We extend the time scale of this analysis by tracking mood word frequencies through the past century of Google book data. We find a general decrease in the use of mood terms through time, which underlies a distinct increase in emotional word usage in American books versus British books in the last half century.

Discussion Using the extraordinary new data on word frequencies in books [2], we find that significant changes in the usage of more generalized mood terms are also detectable through the years. While studies of online social media have shown how short–term patterns in word usage respond to socio-political events [9], [24]–[26], here we find that the expression of moods in books also reflects much longer–term trends of years or even decades. These changes in literary mood are seemingly driven by major century phenomena such as World War II, The Great Depression, or the Baby Boom. We also found a general decrease in emotional word usage in the past decades up to the present, which was observed also in fiction writing on its own. We interpret this as a genuine decrease in the literary expression of emotion, but an alternative explanation could be that mood words have changed, rather than decreased in usage, through the century. This seems unlikely to explain the observed decrease, however, because we used contemporary word lists, analyzed recently in Twitter data to characterize recent events [24], any bias of which should have increased in usage towards the present. Our results also support the popular notion that American authors express more emotion than the British. Somewhat surprisingly, this difference has apparently developed only since the 1960s, and as part of a more general stylistic differentiation in American versus British English, reflected similarly in content-free word frequencies. This relative increase of American mood word use roughly coincides with the increase of anti–social and narcissistic sentiments in U.S. popular song lyrics from 1980 to 2007 [6], as evidenced by steady increases in angry/antisocial lyrics and in the percentage of first-person singular pronouns (e.g., I, me, mine), with a corresponding decrease in words indicating social interactions (e.g., mate, talk, child) over the same 27-year period [6]. As these findings appear to genuinely reflect changes in published language, a remaining question is whether word usage represents real behavior in a population, or possibly an absence of that behavior which is increasingly played out via literary fiction (or online discourse). It has been suggested, for example, that it was the suppression of desire in ordinary Elizabethan English life that increased demand for writing “obsessed with romance and sex” [27]. So while it is easy to conclude that Americans have themselves become more ‘emotional’ over the past several decades, perhaps songs and books may not reflect the real population any more than catwalk models reflect the average body; the observed changes reflect the book market, rather than a direct change in American culture. We believe the changes do reflect changes in culture, however, because unlike lyrics of the top 10 songs, the book data are independent of book sales [2]. Although authors may not be a perfectly representative subset of the general population, at least the Google dataset is not as overtly commercial as song lyrics or any of the other ubiquitous “most popular” lists of online media. Furthermore, the association of mood changes with major century economic and political events supports the fact that word usage, as retrieved from Google dataset, reveals the long term response to these events in a much broader population of book authors. The dynamics of the feedback between book authors and the wider public can be explored by future studies involving the Ngram dataset. In any case, changes in culture consist of changes in cultural artifacts, of which words are an informative sample [2], [6]–[8], [28]–[30]. Future studies will surely explore diversity more closely. A population-level mean – including what we have reported here – does not necessarily track a typical behavior, so the meaning of patterns will become refined by addressing changes cross-culturally (e.g. non-English and non-Western languages), and at the smaller community scale [31]. Another promising development is the analysis of more complex sets of cultural traits that might be more diagnostic than mood words or content-free words. More generally, we hope that we can contribute to the world of Big Data studies by showing that time depth is a crucial dimension. Our results on the long–term, mass scale encourage the more detailed use of word data to characterize the evolution of cultural differences and trends, to detect patterns previously unknown through conventional history [7], [25]. While new theoretical and modelling approaches have rapidly multiplied in the field of cultural evolution (see e.g. [32]–[36]), we believe that the current availability and abundance of quantitative data represents an extraordinary, and much needed, opportunity to provide empirical validation in human cultural dynamics studies.

Methods For this study we assessed the emotional valence of the text in books using a text analysis tool, namely WordNet Affect [37]–[39]. WordNet Affect builds on WordNet [40] by labeling synonymous terms which may represent mood states. Six mood categories, each represented by a different number of terms, have been analyzed: Anger (N = 146), Disgust (N = 30), Fear (N = 92), Joy (N = 224), Sadness (N = 115), and Surprise (N = 41). The text analysis was performed on word stems; the latter were formed using Porter's Algorithm [41]. Both WordNet Affect and Porter's Algorithm are considered as standard tools in text mining and have been applied in several relevant tasks [24], [42]–[46]. We obtained the time series of stemmed word frequencies via Google's Ngram tool (http://books.google.com/ngrams/datasets) in four distinct data sets: 1-grams English (combining both British and American English), 1-grams English Fiction (containing only fiction books), 1-grams American English, and 1-grams British English. For each stemmed word we collected the amount of occurrences (case insensitive) in each year from 1900 to 2000 (both included). We excluded years before 1900 since the number of books before 1900 is considerably lower, and years after 2000 since books published recently are still being included in the data set, and therefore latest records are incomplete and possibly biased. Because the number of books scanned in the data set varies from year to year, to obtain frequencies for performing the analysis we normalized the yearly amount of occurrences using the occurrences, for each year, of the word “the”, which is considered as a reliable indicator of the total number of words in the data set. We preferred to normalize by the word “the”, rather than by the total number of words, to avoid the effect of the influx of data, special characters, etc. that may have come into books recently. The word “the” is about 5–6% of all words, and a good representative of real writing, and real sentences. To test the robustness of the normalization, we also performed the same analysis reported in Figure 1 (differences between -scores (see below) for Joy and Sadness in the 1-grams English data set) using two alternative normalizations, namely the cumulative count of the top 10 most frequent words each year (Figure S2a), and the total counts of 1-grams as in [2] (Figure S2b). The resulting time series are higly correlated (see the legend of Figure S2), confirming the robustness of the normalization. For a year , given the count of the word “the” in the corpus as well as the counts of the WordNet terms representing one of the six considered mood types, we computed a mood score ( ) as follows: (1)i.e. a mood score is essentially the average normalized frequency across the considered mood terms. In order to compare different types of moods effectively, after computing the mood scores for the entire set of years (1900 to 2000), we converted them to their -score equivalent ( ), using: (2)where and denote the mean and standard deviation of the mood scores across the considered set of years. When determining the ‘absolute’ trends of moods (results in Figure 2), to avoid any possible bias related to normalization, we compared the -scores of moods time series with a -score derived by a random sample of stems. This random sample was obtained by extracting random terms from the Part of Speech database, which is a combination of Moby Part-of-Speech II and WordNet database, and contains terms (http://wordlist.sourceforge.net/pos-readme). The terms were stemmed using Porter's Algorithm [41]. Duplicated stems and stems shorter then two letters were finally eliminated obtaining a sample of stems that we used for the analysis. Finally (results in Figure 3), we compared the difference between American English and British English books in three other data sets (in addition to mood terms): content-free words, using the list of the content-free words provided by Huges et al. [7], the same random stems used for Figure 2, and the list of the 100 largest urban agglomerations in the world (from http://en.wikipedia.org/wiki/List_of_urban_agglomerations_by_population_(United_Nations), containing terms like Tokyo, Karachi, and Berlin. Agglomerations with composite names, e.g. New York–Newark, Rio de Janeiro were excluded).

Supporting Information Figure S1. Decrease in the use of emotion-related words through time in fiction books. Difference between -scores of the six emotions and of a random sample of stems (see Methods) for years from 1900 to 2000 (raw data and smoothed trend) in the 1-grams English Fiction data set. Red: the trend for Fear (raw data and smoothed trend), the emotion with the highest final value. Blue: the trend for Disgust (raw data and smoothed trend), the emotion with the lowest final value. Values are smoothed using Friedman's ‘super smoother’ through R function supsmu() [47]. https://doi.org/10.1371/journal.pone.0059030.s001 (TIFF) Figure S2. Historical periods of positive and negative moods with alternative normalizations. Difference between -scores of Joy and Sadness for years from 1900 to 2000 (raw data and smoothed trend). Values above zero indicate generally ‘happy’ periods, and values below the zero indicate generally ‘sad’ periods. Values are smoothed using Friedman's ‘super smoother’ through R function supsmu() [47]. A: Frequencies are normalized using the cumulative count of the top 10 most frequent words for each year. Correlations with the time series used in the analysis (normalized with the yearly count of “the”) are statistically significant (Pearson's for row data, and for smoothed data. In both cases and ). B: Frequencies are normalized using the total counts of 1-grams for each year. Also in this case correlations with the time series used in the analysis are statistically significant (Pearson's for row data, and for smoothed data. In both cases and ). https://doi.org/10.1371/journal.pone.0059030.s002 (TIFF)

Author Contributions Conceived and designed the experiments: AA VL RAB. Analyzed the data: AA VL. Wrote the paper: AA VL PG RAB.