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

This concise volume, using examples of psychotherapy talk, showcases the potential applications of data analytics for advancing discourse research and other related disciplines. The book provides a brief primer on data analytics, defined as the science of analyzing raw data to reveal new insights and support decision making. Currently underutilized in discourse research, Tay draws on the case of psychotherapy talk, in which clients’ concerns are worked through via verbal interaction with therapists, to demonstrate how data analytics can address both practical and theoretical concerns. Each chapter follows a consistent structure, offering a streamlined walkthrough of a key technique, an example case study, and annotated Python code. The volume shows how techniques such as simulations, classification, clustering, and time series analysis can address such issues as incomplete data transcripts, therapist–client (a)synchrony, and client prognosis, offering inspiration for research, training, and practitioner self-reflection in psychotherapy and other discourse contexts. This volume is a valuable resource for discourse and linguistics researchers, particularly for those interested in complementary approaches to qualitative methods, as well as active practitioners.

Table of Contents:
Introduction Defining data analytics Data analytics for discourse analysis The case of psychotherapy talk Outline of the book Quantifying language and implementing data analytics Quantification of language: word embedding Quantification of language: LIWC scores Introduction to Python and basic operations Chapter 2 Monte Carlo simulations Introduction to MCS: bombs, birthdays, and casinos The birthday problem Spinning the casino roulette Case study: Simulating missing or incomplete transcripts Step 1: Data and LIWC scoring Step 2: Simulation runs with a train-test approach Step 3: Analysis and validation of aggregated outcomes Python code used in this chapter Chapter 3 Cluster analysis Introduction to cluster analysis: creating groups for objects Agglomerative hierarchical clustering (AHC) k-means clustering Case study: Measuring linguistic (a)synchrony between therapists and clients Step 1: Data and LIWC scoring Step 2: k-means clustering and model validation Step 3: Qualitative analysis in context Python code used in this chapter Chapter 4 Classification Introduction to classification: predicting groups from objects Case study: Predicting therapy types from therapist-client language Step 1: Data and LIWC scoring Step 2: k-NN and model validation Python code used in this chapter Chapter 5 Time series analysis Introduction to time series analysis: squeezing juice from sugarcane Structure and components of time series data Time series models as structural signatures Case study: Modeling and forecasting psychotherapy language across sessions Step 1: Inspect series Step 2: Compute (P)ACF Step 3: Identify candidate models Step 4: Fit model and estimate parameters Step 5: Evaluate predictive accuracy, model fit, and residual diagnostics Step 6: Interpret models in context Python code used in this chapter Conclusion Data analytics as a rifle and a spade Applications in other discourse contexts Combining data analytic techniques in a project Final words: invigorate, collaborate, and empower