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Exploiting coherence for the simultaneous discovery of latent facets and associated sentiments

Lakkaraju, Himabindu and Bhattacharyya, Chiranjib and Bhattacharya, Indrajit and Merugu, Srujana (2011) Exploiting coherence for the simultaneous discovery of latent facets and associated sentiments. In: 2011 SIAM International Conference on Data Mining, April 28-30, 2011, Mesa, Arizona, USA.

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Official URL: http://drona.csa.iisc.ernet.in/~chiru/papers/sdm_c...

Abstract

Facet-based sentiment analysis involves discovering the latent facets, sentiments and their associations. Traditional facet-based sentiment analysis algorithms typically perform the various tasks in sequence, and fail to take advantage of the mutual reinforcement of the tasks. Additionally,inferring sentiment levels typically requires domain knowledge or human intervention. In this paper, we propose aseries of probabilistic models that jointly discover latent facets and sentiment topics, and also order the sentiment topics with respect to a multi-point scale, in a language and domain independent manner. This is achieved by simultaneously capturing both short-range syntactic structure and long range semantic dependencies between the sentiment and facet words. The models further incorporate coherence in reviews, where reviewers dwell on one facet or sentiment level before moving on, for more accurate facet and sentiment discovery. For reviews which are supplemented with ratings, our models automatically order the latent sentiment topics, without requiring seed-words or domain-knowledge. To the best of our knowledge, our work is the first attempt to combine the notions of syntactic and semantic dependencies in the domain of review mining. Further, the concept of facet and sentiment coherence has not been explored earlier either. Extensive experimental results on real world review data show that the proposed models outperform various state of the art baselines for facet-based sentiment analysis.

Item Type: Conference Paper
Additional Information: Copyright of this article belongs to Industrial and Applied Mathematics.
Keywords: Probabilistic Modeling;Facet-Based Sen-Timent Analysis;Opinion Mining
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Id for Latest eprints
Date Deposited: 18 Mar 2013 09:34
Last Modified: 18 Mar 2013 09:34
URI: http://eprints.iisc.ac.in/id/eprint/46014

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