Mining contentious documents

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 analyze the underlying divergent arguing expressions that may be present in a collection of contentious documents. It extends the original Latent Dirichlet Allocation, 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 analyses 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. Furthermore, the coherence of these arguing expressions is proved to be of a high quality when evaluated on the basis of recently introduced automatic coherence measure.