# Helping users discover perspectives: Enhancing opinion mining with joint topic models

arXiv: https://arxiv.org/abs/2010.12505 · HTML: https://mlsanity.com/paper/2010.12505/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Structuring the Space of Perspectives](https://mlsanity.com/paper/2608.12113/) (2026)
- [Navigating the Thin Line: Examining User Behavior in Search to Detect Engagement and Backfire Effects](https://mlsanity.com/paper/2401.11201/) (2024)

## Abstract (arXiv)

Support or opposition concerning a debated claim such as abortion should be legal can have different underlying reasons, which we call perspectives. This paper explores how opinion mining can be enhanced with joint topic modeling, to identify distinct perspectives within the topic, providing an informative overview from unstructured text. We evaluate four joint topic models (TAM, JST, VODUM, and LAM) in a user study assessing human understandability of the extracted perspectives. Based on the results, we conclude that joint topic models such as TAM can discover perspectives that align with human judgments. Moreover, our results suggest that users are not influenced by their pre-existing stance on the topic of abortion when interpreting the output of topic models.