Mining Contentious Documents Using an Unsupervised Topic Model Based Approach

@article{Trabelsi2014MiningCD,
  title={Mining Contentious Documents Using an Unsupervised Topic Model Based Approach},
  author={Amine Trabelsi and Osmar R. Za{\"i}ane},
  journal={2014 IEEE International Conference on Data Mining},
  year={2014},
  pages={550-559}
}
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… CONTINUE READING

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