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Awesome Topic Modeling

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All methods, libraries and resources for exploring topics from large datasets

📚 Introduction

Topic Models are a collection of machine learning models that explores topics in a large set of documents

🏛️ Classical Topic Models

This part includes classical topic models, which heavily based on statistical models and com

🤖 Neural Topic Models

This part revolves around topic modelling techniques with the adoption of deep learning models.

  • Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence [Paper] [Github]
  • Topic Modeling with Contextualized Word Representation Clusters [Paper]
  • TopicBERT: Topic-aware BERT for Efficient Document Classification [Paper] [Github]
  • BERTopic [Paper] [Github]

🌱 Semi-supervised topic models (SSTM)

SSTM allows users to inject prior knowledge about topics into topic models

⚡ Dynamic Topic Models (DTM)

DTMs are set of topic models that take into account the evolution of topic through time

  • Dynamic Topic Model, David Blei [Paper] [Github]
  • Dynamic Non-negative Matrix Factorization (Dynamic NMF) [Paper] [Github]

Topic Model Libraries

  • OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track) [Github]
  • Topic modeling using Gensim [Article]

Surveys:

  • Topic Modelling Meets Deep Neural Networks: A Survey [Paper]