Avoid violence, rioting, and outrage; approach celebration, delight, and strength: Using large text corpora to compute valence, arousal, and the basic emotions

Abstract

Ever since Aristotle discussed the issue in Book II of his Rhetoric, humans have attempted to identify a set of "basic emotion labels". In this paper we propose an algorithmic method for evaluating sets of basic emotion labels that relies upon computed co-occurrence distances between words in a 12.7-billion-word corpus of unselected text from USENET discussion groups. Our method uses the relationship between human arousal and valence ratings collected for a large list of words, and the co-occurrence similarity between each word and emotion labels. We assess how well the words in each of 12 emotion label sets-proposed by various researchers over the past 118 years-predict the arousal and valence ratings on a test and validation dataset, each consisting of over 5970 items. We also assess how well these emotion labels predict lexical decision residuals (LDRTs), after co-varying out the effects attributable to basic lexical predictors. We then demonstrate a generalization of our method to determine the most predictive "basic" emotion labels from among all of the putative models of basic emotion that we considered. As well as contributing empirical data towards the development of a more rigorous definition of basic emotions, our method makes it possible to derive principled computational estimates of emotionality-specifically, of arousal and valence-for all words in the language.

The principals of meaning: Extracting semantic dimensions from co-occurrence models of semantics

“…Arousal judgments from the ANEW norms set (Bradley & Lang, 1999) correlated with those in the Warriner et al (2013) norms set at r = .76 (as compared to .95 for valence and .80 for dominance). Attempts at algorithmically extrapolating human judgments have likewise found that human judgments of arousal are much less predictable than other semantic measures (Hollis & Westbury, 2016;Mandera, Keuleers, & Brysbaert, 2015;Recchia & Louwerse, 2015;Westbury et al, 2015). These findings, along with the results of our analysis of the skip-gram model, suggest that the concept of arousal is not as clearly specified as a semantic construct.…”

“…For instance, the similarity of meaning between two words can be assessed by measuring the similarity between their co-occurrence vectors. Cooccurrence vectors contain enough information to pass tests for basic verbal ability (e.g., Landauer & Dumais, 1997), to accurately predict human judgments of valence and arousal (e.g., Hollis & Westbury, 2016;Mandera, Keuleers, & Brysbaert, 2015;Recchia & Louwerse, 2015;Westbury, Keith, Briesemeister, Hofmann, & Jacobs, 2015), and to account for behavioral effects of high-level lexical properties such as subjective familiarity (Westbury, 2014) and imageability (Westbury et al, 2013).…”

On Elementary Affective Decisions: To Like Or Not to Like, That Is the Question

“…Can we tentatively infer that EADs to words are determined by their associations to five basic emotions then (Westbury et al, 2014)? Figure 1 illustrating the decision tree data for Model 3 suggests an even simpler answer: to obtain 99% correct EADs, the model requires only two questions.…”

“…A valence value would thus be computed from (1) neural activation patterns distributed over the sensory-motor representations of a word’s referents ( experiential aspect) and (2) the linguistic company the words keep (Harris, 1951), i.e., the size and density of their context ( distributional aspect; Jacobs et al, 2015), as computationally modeled using co-occurrence statistics (Hofmann and Jacobs, 2014). In favor of this view, Westbury et al (2014) recently showed that valence ratings of words can be predicted by their associations to a selected set of emotion labels, derived from theories of basic emotions (cf. also Hofmann and Jacobs, 2014).…”

Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis

“…In addition, future studies will focus on developing new indices of sentiment for inclusion in SEANCE and additional valence features. Such indices will be based on sentiment dictionaries that are currently available, those that become available over time (e.g., the Warriner norms; Warriner, Kuperman, & Brysbaert, 2013;Westbury, Keith, Briesemeister, Hofmann, & Jacobs, 2015), or previous dictionaries that are updated. We also plan to add valence features, as necessary, to examine discourse features such as intensification.…”