Amplifying Participant Voices Through Text Mining

Abstract

Text mining presents an efficient, scalable method to separate signals and noise in large-scale text data, and therefore to effectively analyze open-ended survey responses as well as the tremendous amount of text that students, faculty, and staff produce through their interactions online. Traditional qualitative methods are impractical when working with these data, and text mining methods are consonant with current literature on thematic analysis. This chapter provides a tutorial for researchers new to this method, including a lengthy discussion of preprocessing tasks and knowledge extraction from both supervised and unsupervised activities, potential data sources, and the range of software (both proprietary and open-source) available to them. Examples are provided throughout the paper of text mining at work in two studies involving data collected from college students. Limitations of this method and implications for future research and policy are discussed.

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