Topic modeling is used to analyze the distribution of words in documents. It assigns sets of words to “topics,” where each document contains one or more topics. Usually the topic assignments for words and documents is done using unsupervised methods, and doesn't correspond to a particular definition of topics. A popular method for topic modeling is Latent Dirichlet Allocation (LDA, Blei 2003).

• Wikipedia - Topic Model
• Blei 2012 - Introduction to Probabilistic Topic Models
• Surveys
  • Churchill & Singh 2022 - The Evolution of Topic Modeling

• Classic Papers
  • Blei et al 2003 - Latent Dirichlet Allocation
• Recent papers
  • Schofield et al 2017 - Pulling Out the Stops: Rethinking Stopword Removal for Topic Models
  • Ding et al 2018 - Coherence-Aware Neural Topic Modeling
  • Song et al 2020 - Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus
  • Cheevaprawatdomrong et al 2021 - More Than Words: Collocation Tokenization for Latent Dirichlet Allocation Models
  • Grootendorst et al 2022 - BERTopic: Neural topic modeling with a class-based TF-IDF procedure
• Applications
  • Chambers & Jurafsky 2011 - Template-Based Information Extraction without the Templates

• Gensim blog post

• David Blei
• Alexandra Schofield

• Graphical Models