Sentiment Analysis of Linguistic Data in Behavioral Research

Ian Cero, Jiebo Luo and John Falligant
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Ian Cero: University of Rochester Medical Center

No gw97k, OSF Preprints from Center for Open Science

Abstract: A complete science of human behavior requires a comprehensive account of the verbal behavior those humans exhibit. Existing behavioral theories of such verbal behavior have produced compelling insight into language’s underlying function, but the expansive program of research those theories deserve has unfortunately been slow to develop. We argue that the status quo’s manually implemented and study-specific coding systems are too resource intensive to be worthwhile for most behavior analysts. These high input costs in turn discourage research on verbal behavior overall. We propose lexicon-based sentiment analysis as a more modern and efficient approach to the study of human verbal products, especially naturally-occurring ones (e.g., psychotherapy transcripts, social media posts). In the present discussion, we introduce the reader to principles of sentiment analysis, highlighting its usefulness as a behavior analytic tool for the study of verbal behavior. We conclude with an outline of approaches for handling some of the more complex forms of speech, like negation, sarcasm, and speculation. The appendix also provides a worked example of how sentiment analysis could be applied to existing questions in behavior analysis, complete with code that readers can incorporate into their own work.

Date: 2023-05-01
New Economics Papers: this item is included in nep-big, nep-evo and nep-hme
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:gw97k

DOI: 10.31219/osf.io/gw97k

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