GESIS Training Courses
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Scientific Coordination

Dr.
Sabina Haveric
Tel: +49 (0221) 47694 - 166

Administrative Coordination

Laura Rüwe

Introduction to Topic Modeling

Lecturer(s):
Dr. Wouter van Atteveldt, Dr. Kasper Welbers

Date: 14.11 - 15.11.2019 ics-file

About the lecturer - Dr. Wouter van Atteveldt

About the lecturer - Dr. Kasper Welbers

Course description

In the first day, we will introduce topic modeling and the principles of automatic text analysis and topic modeling. We will explain the basic assumptions of bag-of-words analysis, unsupervised clustering, and the dirichlet distribution. We will use the quanteda and topicmodels packages for doing the analyses and LDAviz and corpustools for visualization and validation.

The seciond day we will first look in depth at how fitting an LDA model with Gibbs sampling actually works and look at the various parameters and choices. We will also look at linguistic preprocessing using the spacy package. Finally, we will introduce alternative topic models, from Dynamic and Correlated topic models to Structural Topic Models. We will use the stm package to show how to estimate a structural topic model with time or source as covariates, and show how to analyse and interpret the results.


Learning objectives

Students participating in the first day will learn the basics of R. All students will understand the principles and working of topic modeling and (unsupservised) text analysis in general. Students will be able to use R for running LDA and Structural Topic Models, and interpret and visualize the results.


Prerequisites

No specific prior knowledge is required, but a basic knowledge of math and statistics will help understand the algorithms. Participants without knowledge of R are strongly advised to install R and RStudio beforehand and make themselves familiar with the software. All participants are advised to browse through chapters 9-16 of R4DS (https://r4ds.had.co.nz/.).


Schedule

Recommended readings

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