A data visualization that shows clustered words in structured interrelationships as branches on a tree (may be horizontal or vertical).
Published in Chapter:
Using Computational Text Analysis to Explore Open-Ended Survey Question Responses
Copyright: © 2019
|Pages: 33
DOI: 10.4018/978-1-5225-8563-3.ch007
Abstract
To capture a broader range of data than close-ended questions (often defined and delimited by the survey instrument designer), open-ended questions, such as text-based elicitations (and file-upload options for still imagery, audio, video, and other contents) are becoming more common because of the wide availability of computational text analysis, both within online survey tools and in external software applications. These computational text analysis tools—some online, some offline—make it easier to capture reproducible insights with qualitative data. This chapter explores some analytical capabilities, in matrix queries, theme extraction (topic modeling), sentiment analysis, cluster analysis (concept mapping), network text structures, qualitative cross-tabulation analysis, manual coding to automated coding, linguistic analysis, psychometrics, stylometry, network analysis, and others, as applied to open-ended questions from online surveys (and combined with human close reading).