Improving the TENOR of Labeling: Re-evaluating Topic Models for Content
Analysis
- URL: http://arxiv.org/abs/2401.16348v2
- Date: Tue, 20 Feb 2024 03:10:58 GMT
- Title: Improving the TENOR of Labeling: Re-evaluating Topic Models for Content
Analysis
- Authors: Zongxia Li, Andrew Mao, Daniel Stephens, Pranav Goel, Emily Walpole,
Alden Dima, Juan Fung, Jordan Boyd-Graber
- Abstract summary: We conduct the first evaluation of neural, supervised and classical topic models in an interactive task based setting.
We show that current automated metrics do not provide a complete picture of topic modeling capabilities.
- Score: 5.757610495733924
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Topic models are a popular tool for understanding text collections, but their
evaluation has been a point of contention. Automated evaluation metrics such as
coherence are often used, however, their validity has been questioned for
neural topic models (NTMs) and can overlook a models benefits in real world
applications. To this end, we conduct the first evaluation of neural,
supervised and classical topic models in an interactive task based setting. We
combine topic models with a classifier and test their ability to help humans
conduct content analysis and document annotation. From simulated, real user and
expert pilot studies, the Contextual Neural Topic Model does the best on
cluster evaluation metrics and human evaluations; however, LDA is competitive
with two other NTMs under our simulated experiment and user study results,
contrary to what coherence scores suggest. We show that current automated
metrics do not provide a complete picture of topic modeling capabilities, but
the right choice of NTMs can be better than classical models on practical task.
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