Abstract
Practitioners in many domains–e.g., clinical psychologists, college instructors, researchers–collect written responses from clients. A well-developed method that has been applied to texts from sources like these is the computer application Linguistic Inquiry and Word Count (LIWC). LIWC uses the words in texts as cues to a person’s thought processes, emotional states, intentions, and motivations. In the present study, we adopt analytic principles from LIWC and develop and test an alternative method of text analysis using naïve Bayes methods. We further show how output from the naïve Bayes analysis can be used for mark up of student work in order to provide immediate, constructive feedback to students and instructors.
 References
 
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Highlights
The linguist, Edward Sapir, believed that “language and our thought-grooves are inextricably interwoven, [and] are, in a sense, one and the same”
In our approach to text analysis, we adopt the basic assumption underlying Linguistic Inquiry and Word Count (LIWC), which is that words in a text can function as statistical variables and thereby provide the basis for quantitative analysis
Machine Tools for Text Analysis Machine tools for analyzing the content of language samples are based on the general assumption that aspects of the semantic structure of text can be recovered through algorithmic methods
Summary
The linguist, Edward Sapir, believed that “language and our thought-grooves are inextricably interwoven, [and] are, in a sense, one and the same” (in Salzmann, 2004, p. 43). Whereas early work was slow and tedious, recent advances in technology have enabled analyses of large language samples from sources like product reviews, forums, blogs, social networks, and mental health settings. These analyses have been used productively to achieve a variety of goals, for example, in business for sentiment analysis, and in clinical settings to treat depression. A successful and widely applied machine tool for text analysis is Linguistic Inquiry and Word Count (LIWC) (Pennebaker, Boyd, Jordan, & Blackburn, 2015). Machine Tools for Text Analysis Machine tools for analyzing the content of language samples are based on the general assumption that aspects of the semantic structure of text can be recovered through algorithmic methods. In the two subsections, we briefly describe LIWC, which provides a framework within which to understand the methods that we develop, and naïve
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