Open AccessMethods for Classification of Text Data: Can the Potential of Quantitative Analysis Be Applied to Qualitative Research?
Author(s) -
Marina Yurievna Aleksandrova
Publication year - 2021
Publication title -
inter/interakciâ. intervʹû. interpretaciâ
Language(s) - English
Resource type - Journals
eISSN - 2687-0401
pISSN - 2307-2075
DOI - 10.19181/inter.2021.13.2.5
Subject(s) - computer science , field (mathematics) , task (project management) , recall , data science , precision and recall , natural language processing , information retrieval , artificial intelligence , data mining , psychology , cognitive psychology , mathematics , management , pure mathematics , economics
Text mining has developed rapidly in recent years. In this article we compare classification methods that are suitable for solving problems of predicting item nonresponse. The author builds reasoning about how the analysis of textual data can be implemented in a wider research field based on this material. The author considers a number of metrics adapted for textual analysis in the social sciences: accuracy, precision, recall, F1-score, and gives examples that can help a sociologist figure out which of them is worth paying attention depending on the task at hand (classify text data with equal accuracy, or more fully describe one of the classes of interest). The article proposes an analysis of results obtained by analyzing texts based on the materials of the European Social Survey (ESS).