Paper: A Joint Model of Text and Aspect Ratings for Sentiment Summarization

ACL ID P08-1036
Title A Joint Model of Text and Aspect Ratings for Sentiment Summarization
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

Online reviews are often accompanied with numerical ratings provided by users for a set of service or product aspects. We propose a statistical model which is able to discover corresponding topics in text and extract tex- tual evidence from reviews supporting each of these aspect ratings – a fundamental problem in aspect-based sentiment summarization (Hu and Liu, 2004a). Our model achieves high ac- curacy, without any explicitly labeled data ex- cept the user provided opinion ratings. The proposed approach is general and can be used for segmentation in other applications where sequential data is accompanied with corre- lated signals.