Text Mining Tutorial

Abstract

A growing challenge facing scholars who study group processes is textual data overload. The immense amount of text generated by group members’ interactions via email, text messages, and social media can be a barrier during data collection and analysis. Instead of scaling back textual data collection, group process scholars can make use of text mining, a computational approach to finding patterns within and extracting information of interest from textual datasets. This tutorial provides an entry-level introduction to the text mining approach in terms of how it works, its underlying assumptions, the basic steps of analysis, and decisions that must be made during the text mining process from data collection to final interpretation. The approach is demonstrated using a real-world dataset consisting of transcriptions of medical consultation conversations among groups of emergency department physicians. The results demonstrate the potential benefits of a data-driven approach to analysis of textual datasets.