title: Sentiment Analysis and Social Cognition Engine (SEANCE): An automatic tool for sentiment, social cognition, and social-order analysis.
abstract: This study introduces the Sentiment Analysis and Cognition Engine (SEANCE), a freely available text analysis tool that is easy to use, works on most operating systems (Windows, Mac, Linux), is housed on a user's hard drive (as compared to being accessed via an Internet interface), allows for batch processing of text files, includes negation and part-of-speech (POS) features, and reports on thousands of lexical categories and 20 component scores related to sentiment, social cognition, and social order. In the study, we validated SEANCE by investigating whether its indices and related component scores can be used to classify positive and negative reviews in two well-known sentiment analysis test corpora. We contrasted the results of SEANCE with those from Linguistic Inquiry and Word Count (LIWC), a similar tool that is popular in sentiment analysis, but is pay-to-use and does not include negation or POS features. The results demonstrated that both the SEANCE indices and component scores outperformed LIWC on the categorization tasks.
authors.affiliation: Department of Applied Linguistics/ESL, Georgia State University, 25 Park Place, Suite 1500, Atlanta, GA, 30303, USA. scrossley@gsu.edu.
authors.forename: Scott A
authors.initials: SA
authors.lastname: Crossley
authors.affiliation: Department of Applied Linguistics/ESL, Georgia State University, 25 Park Place, Suite 1500, Atlanta, GA, 30303, USA.
authors.forename: Kristopher
authors.initials: K
authors.lastname: Kyle
authors.affiliation: Department of Psychology, Learning Sciences Institute, Arizona State University, Tempe, AZ, USA.
authors.forename: Danielle S
authors.initials: DS
authors.lastname: McNamara
mesh_terms: D003071:Cognition; D057225:Data Mining; D004644:Emotions; D006801:Humans; D012984:Software
keywords: Affect detection; Automatic tools; Corpus linguistics; Natural language processing; Opinion mining; Sentiment analysis