Evaluating Dynamic Topic Models
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Abstract (arXiv)
There is a lack of quantitative measures to evaluate the progression of topics through time in dynamic topic models (DTMs). Filling this gap, we propose a novel evaluation measure for DTMs that analyzes the changes in the quality of each topic over time. Additionally, we propose an extension combining topic quality with the model's temporal consistency. We demonstrate the utility of the proposed measure by applying it to synthetic data and data from existing DTMs. We also conducted a human evaluation, which indicates that the proposed measure correlates well with human judgment. Our findings may help in identifying changing topics, evaluating different DTMs, and guiding future research in this area.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 10 of its 64 references that are in the map. First posted on arXiv 12 Sep 2023.
Builds on
10 of 64 references.
References · 10
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
- 2019Topic Modeling in Embedding Spacescited by 224
- 2021Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherencecited by 74
- 2020Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!cited by 73
- 2019The Dynamic Embedded Topic Modelcited by 41
- 2023A Call for Standardization and Validation of Text Style Transfer Evaluationcited by 14
- 2018Scalable Generalized Dynamic Topic Modelscited by 9
- 2016Scaling up Dynamic Topic Modelscited by 8
- 2023Learning to Play Text-based Adventure Games with Maximum Entropy Reinforcement Learningcited by 2
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2024On the Challenges and Opportunities in Generative AIcited by 31
- 2025Stick-Breaking Embedded Topic Model with Continuous Optimal Transport for Online Analysis of Document Streamscited by 0
- 2026BERTilda: Explainable Topic Lifecycle Tracking with Split/Merge Detection via Similarity-and-Flow Temporal Graphscited by 0
- 2026Dynamic Topic Modeling for Cross-Corpus Temporal Analysiscited by 0
- 2026Dynamic Topic Modeling for Cross-Corpus Temporal Analysis24 Aug 2026
- 2026BERTilda: Explainable Topic Lifecycle Tracking with Split/Merge Detection via Similarity-and-Flow Temporal Graphs9 Jun 2026
- 2025Stick-Breaking Embedded Topic Model with Continuous Optimal Transport for Online Analysis of Document Streams21 Oct 2025
- 2024On the Challenges and Opportunities in Generative AI28 Feb 2024