Perspective Discovery in Controversial Debates

Perspective Discovery in Controversial Debates: An exploration of unsupervised topic models

Liu, J.C.M. (TU Delft Electrical Engineering, Mathematics and Computer Science)

Tintarev, N. (mentor)
Houben, G.J.P.M. (graduation committee)
Finavaro Aniche, M. (graduation committee)
Draws, T.A. (mentor)

Delft University of Technology

Computer Science

2020-08-27

Since the introduction of the Web, online platforms have become a place to share opinions across various domains (e.g., social media platforms, discussion fora or webshops). Consequently, many researchers have seen a need to classify, summarise or categorise these large sets of unstructured user-generated content. A field related to this task is also known as opinion mining in which various applications have focused on sentiment analysis techniques to classify opinionated documents based on sentiment. More recent, researchers have focused on stance classification to classify opinionated documents based on stance in controversial debates. However, in the case of such controversial debates it would be equally interesting to know the underlying reasons behind a stance in order to truly understand a discussion. We can call these underlying reasons as perspectives. Few have focused on distilling such perspectives from text and in this research we aim to explore the use of an unsupervised model - called joint topic models - to perform the task of perspective discovery. We define perspective discovery on a controversial debate as the process of automatically finding and extracting a structured overview of perspectives from unstructured text. The aim is to quantify how well existing joint topic models can extract human understandable perspectives between and within stances for more fine-grained opinion mining on textual debates. To perform this evaluation we propose an evaluation setup with an extensive user study. This setup focuses on the topic model’s clustering ability of perspectives as well as the human understandability of the topic model’s output. Based on the results we may derive that topic models can discover some of the perspectives from text. Moreover, the results suggest that users are not influenced by their pre-existing stance when interpreting the output of topic models.

sentiment analysis
controversial debates
topic modelling
joint topic modelling
opinion mining

http://resolver.tudelft.nl/uuid:7c09a506-7bc0-4b26-a2f3-5f69ceeb74f4

Student theses

master thesis

© 2020 J.C.M. Liu