A Joint Model of Text and Aspect Ratings for Sentiment Summarization

Ivan Titov, Ryan T. McDonald

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 textual 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 accuracy, without any explicitly labeled data except 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 correlated signals.
Original languageEnglish
Title of host publicationACL 2008, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, June 15-20, 2008, Columbus, Ohio, USA
PublisherAssociation for Computational Linguistics
Pages308-316
Number of pages9
Publication statusPublished - 2008

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