Finding Arguing Expressions of Divergent Viewpoints in Online Debates
Amine Trabelsi, Osmar R. Zai͏̈ane · 2014
This work suggests a fine-grained mining of contentious documents, specifically online debates, towards a summarization of contention issues.We propose a Joint Topic Viewpoint model (JTV) for the unsupervised identification and the clustering of arguing expressions according to the latent topics they discuss and the implicit viewpoints they voice.A set of experiments is conducted on online debates documents.Qualitative and quantitative evaluations of the model's output are performed in context of different contention issues.Analysis of experimental results shows the effectiveness of the proposed model to automatically and accurately detect recurrent patterns of arguing expressions in online debate texts.