Title :
Mining Contentious Documents Using an Unsupervised Topic Model Based Approach
Author :
Trabelsi, Amine ; Zaiane, Osmar R.
Author_Institution :
Dept. of Comput. Sci., Univ. of Alberta, Edmonton, AB, Canada
Abstract :
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 Word Net 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-of-the-art method. Moreover, the ability to automatically generate distinctive and informative patterns of arguing expressions is demonstrated.
Keywords :
data mining; document handling; probability; JTV probabilistic model; LDA; Word Net coverage; arguing expression; contentious document mining; editorials; joint topic viewpoint probabilistic model; latent Dirichlet allocation; online debates; opinion mining; polls; qualitative analysis; quantitative analysis; unsupervised grouping; unsupervised method; unsupervised topic model based approach; Data mining; Data models; Editorials; Government; Insurance; Joints; Medical services;
Conference_Titel :
Data Mining (ICDM), 2014 IEEE International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4799-4303-6
DOI :
10.1109/ICDM.2014.120