Data Analytics for Discourse Analysis with Python

ISBN-13: 9781032419015 / Twarda / 2024 / 160 str.

Dennis Tay

Opis

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

Spis treści

Contents

 

List of Figures

List of Tables

 

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

 

Index

O autorze

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