Word Embeddings Reveal How Fundamental Sentiments Structure Natural Language

Central to affect control theory are culturally shared meanings of concepts. That these sentiments overlap among members of a culture presumably reflects their roots in the language use that members observe. Yet, the degree to which the affective meaning of a concept is encoded in the way linguistic representations of that concept are used in everyday symbolic exchange has yet to be demonstrated. The question has methodological as well as theoretical significance for affect control theory, as language may provide an unobtrusive, behavioral method of obtaining EPA ratings complementary to those heretofore obtained via questionnaires. We pursue a series of studies that evaluate whether tools from machine learning and computational linguistics can capture the fundamental affective meaning of concepts from large text corpora. We develop an algorithm that uses word embeddings to predict EPA profiles available from a recent EPA dictionary derived from traditional questionnaires, as well as novel concepts collected using an open-source web app we have developed. Across both a held-out portion of the available data as well as the novel data, our best predictions correlate with survey-based measures of the E, P, and A ratings of concepts at a magnitude greater than 0.85, 0.8, and 0.75 respectively.