Dennis Tay

Data Analytics for Discourse Analysis with Python (eBook, PDF)

The Case of Therapy Talk

• Format: PDF

Produktbeschreibung

This concise volume, using examples of psychotherapy talk, showcases the potential applications of data analytics for advancing discourse research and other related disciplines.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 190
• Erscheinungstermin: 19. April 2024
• Englisch
• ISBN-13: 9781040007693
• Artikelnr.: 70044086

Autorenporträt

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).

Inhaltsangabe

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