Helping users discover perspectives

Enhancing opinion mining with joint topic models

Conference paper (2021)

Authors

T.A. Draws Web Information Systems - EEMCS
Jody Liu Student
N. Tintarev Web Information Systems - EEMCS
Research Group
Web Information Systems (EEMCS) (TU Delft)
Copyright: © 2021 T.A. Draws, Jody Liu, N. Tintarev
DOI: https://doi.org/10.1109/ICDMW51313.2020.00013
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Web Information Systems

Abstract

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.

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