View Proposal

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Proposer

Pierre Le Bras

Title

Comparison of Topic Model Visualisations

Goal

To conduct an empirical user evaluation of visualisation systems for Topic Model data.

Description

The data generated by topic models is a rich multi-dimensional set of probabilities, which naturally poses challenges when presented to non-expert users. Over the years, several systems have been built to allow this data exploration by visualising the output of topic models, for example: LDAVis, BERTopic, Topic Mapping (see URL). This project proposes to establish the affordances and hinderances of these many systems empirically by designing a user-based study to quantitatively and qualitatively measuring key metrics. The project would preferably involve the creation of interactive interfaces (one per method) and the iterative development of a rigorous user study, followed by the evaluation of results.

Resources

Grootendorst, M., 2022. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794. Le Bras, P., Gharavi, A., Robb, D.A., Vidal, A.F., Padilla, S. and Chantler, M.J., 2020. Visualising covid-19 research. arXiv preprint arXiv:2005.06380. Sievert, C. and Shirley, K., 2014, June. LDAvis: A method for visualizing and interpreting topics. In Proceedings of the workshop on interactive language learning, visualization, and interfaces (pp. 63-70).

Background

Url

External Link

Difficulty Level

High

Ethical Approval

InterfaceOnly

Number Of Students

1

Supervisor

Pierre Le Bras

Keywords

topic model, visualisation, user study

Degrees

Bachelor of Science in Computer Science
Master of Engineering in Software Engineering
Master of Science in Artificial Intelligence
Master of Science in Data Science
Master of Science in Software Engineering
Bachelor of Science in Computing Science