Mining Contentious Documents Using an Unsupervised Topic Model Based Approach
- Amine Trabelsi
- Osmar R. Zaiane, University of Alberta (Database)
This work proposes an unsupervised method intended to enhance the quality of opinion mining in contentious text. It presents a Joint Topic Viewpoint (JTV) probabilistic model to analyse the underlying divergent arguing expressions that may be present in a collection of contentious documents. It extends the original Latent Dirichlet Allocation (LDA), which makes it domain and thesaurus-independent, e.g., does not rely on WordNet coverage. The conceived JTV has the potential of automatically carrying the tasks of extracting associated terms denoting an arguing expression, according to the hidden topics it discusses and the embedded viewpoint it voices. Furthermore, JTV’s structure enables the unsupervised grouping of obtained arguing expressions according to their viewpoints, using a constrained clustering approach. Experiments are conducted on three types of contentious documents: polls, online debates and editorials. The qualitative and quantitative analysis of the experimental results show the effectiveness of our model to handle six different contentious issues when compared to a state-ofthe-art method. Moreover, the ability to automatically generate distinctive and informative patterns of arguing expressions is demonstrated.
Citation
A. Trabelsi, O. Zaiane. "Mining Contentious Documents Using an Unsupervised Topic Model Based Approach". IEEE International Conference on Data Mining (ICDM), December 2014.| Keywords: | |
| Category: | In Conference |
| Web Links: | Webdocs |
BibTeX
@incollection{Trabelsi+Zaiane:ICDM14,
author = {Amine Trabelsi and Osmar R. Zaiane},
title = {Mining Contentious Documents Using an Unsupervised Topic Model Based
Approach},
booktitle = {IEEE International Conference on Data Mining (ICDM)},
year = 2014,
}Last Updated: November 13, 2019Submitted by Sabina P
