Data Analytics for Discourse Analysis with Python: the Case of Therapy Talk

Data analytics is the process of examining, cleaning, transforming, and modeling data to extract useful information, identify patterns, and support decision-making.In this field, the data available for analysis includes not only statistical numbers but also textual corpora.In recent years, the development of data analytics, especially text mining, has provided novel approaches for quantitative research in linguistics, including discourse analysis.The book under review, featuring popular text-mining techniques and an abundance of psychotherapeutic texts, constitutes a timely contribution to quantitative discursive research.The volume consists of six chapters, including an introduction and a conclusion.The introductory chapter starts with the definition of data analytics and its interdisciplinary significance, with a spotlight on its relevance to discourse analysis, especially the analysis of psychotherapy talks.It then outlines methods used in this book for quantifying the features of language and texts, such as word embeddings and LIWC (Linguistic Inquiry and Word Count, p. 18), and introduces Python as a basic tool for datadriven linguistic analysis.Here, the author provides many examples to explain complex linguistic features in an easy-to-understand manner.For example, the author uses visualization forms to illustrate the basics of word vector calculation and how this can be used to measure the similarity between words.When explaining LIWC, the author provides specific examples to demonstrate how to quantify the emotional and cognitive features of language.The introductory chapter is particularly informative and methodologically useful for readers who are interested in learning research methods that are complementary to traditional qualitative approaches for discourse analysis and linguistics study at large.The main body of the volume comprises Chapters Two to Five, each dedicated to a distinct analytical technique.These chapters are thoughtfully