Evaluating Dynamic Topic Models
Computation and Language
2023-09-19 v1 Information Retrieval
Machine Learning
Abstract
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.
Cite
@article{arxiv.2309.08627,
title = {Evaluating Dynamic Topic Models},
author = {Charu James and Mayank Nagda and Nooshin Haji Ghassemi and Marius Kloft and Sophie Fellenz},
journal= {arXiv preprint arXiv:2309.08627},
year = {2023}
}