GESIS Training Courses
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Wiss. Koordination

Julia Leesch
Tel: +49 221 47694-169

Administrative Koordination

Laura Rüwe

Introduction to Topic Modeling

Dozent(en):
Dr. Wouter van Atteveldt, Dr. Kasper Welbers

Datum: 14.11 - 15.11.2019 ics-Datei

Referenteninformationen - Dr. Wouter van Atteveldt

Referenteninformationen - Dr. Kasper Welbers

Seminarinhalt

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.


Lernziel

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.


Voraussetzungen

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/.).


Literaturempfehlungen

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