Paper: A Bayesian Model for Joint Unsupervised Induction of Sentiment, Aspect and Discourse Representations

ACL ID P13-1160
Title A Bayesian Model for Joint Unsupervised Induction of Sentiment, Aspect and Discourse Representations
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

We propose a joint model for unsuper- vised induction of sentiment, aspect and discourse information and show that by in- corporating a notion of latent discourse re- lations in the model, we improve the pre- diction accuracy for aspect and sentiment polarity on the sub-sentential level. We deviate from the traditional view of dis- course, as we induce types of discourse re- lations and associated discourse cues rel- evant to the considered opinion analysis task; consequently, the induced discourse relations play the role of opinion and as- pect shifters. The quantitative analysis that we conducted indicated that the integra- tion of a discourse model increased the prediction accuracy results with respect to the discourse-agnostic approach and the qualitative analysis suggests that the in-...