Topic modeling with Latent Dirichlet Allocation

Routledge eBooks 2026 Book chapter

View at publisher
Authors: Christian Schneijderberg, Oliver Wieczorek, Isabel Steinhardt
DOI: 10.4324/9781003496397-20
Cited by: 0

Abstract

In this section, we introduce you to topic modelling with Latent Dirichlet Allocation (LDA). LDA is a generative model which relies on the bag-of-words approach. LDA assumes that the distribution of topics across documents and tokens results from an underlying process, giving rise to abstracts topics. Against this backdrop, this chapter aims to equip you with the knowledge to understand the LDA algorithm, preprocess the text corpus, automatize topic extraction, select topics for interpretation, calculate and visualize goodness of fit measures, and how to effectively interpret topics.

Fields of Study & Subjects

Topic Modeling Computational and Text Analysis Methods Advanced Text Analysis Techniques

Publication details

Journal / source
Routledge eBooks
Pages
525–557
Publication date
15 January 2026
Language
EN
Document type
Book chapter