Data Analytics for Discourse Analysis with Python: Routledge Studies in Linguistics

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

Dennis Tay is Professor at the Department of English and Communication, the Hong Kong Polytechnic University. He is Co-Editor-in-Chief of Metaphor and the Social World, Associate Editor of Metaphor and Symbol, Academic Editor of PLOS One, and Review Editor of Cognitive Linguistic Studies. His recent Routledge publication is Time Series Analysis of Discourse: Method and Case Studies (2020).