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