Sentiment Learning on Product Reviews via Sentiment Ontology Tree

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 404–413, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Sentiment Learning on Product Reviews via Sentiment Ontology Tree Wei Wei Department of Computer and Information Science Norwegian University of Science and Technology wwei@idi.ntnu.no Jon Atle Gulla Department of Computer and Information Science Norwegian University of Science and Technology jag@idi.ntnu.no Abstract Existing works on sentiment analysis on product reviews suffer from the following limitations: (1) The knowledge of hierar- chical relationships of products attributes is not fully utilized. (2) Reviews or sen- tences mentioning several attributes asso- ciated with complicated sentiments are not dealt with very well. In this paper, we pro- pose a novel HL-SOT approach to label- ing a product’s attributes and their asso- ciated sentiments in product reviews by a Hierarchical Learning (HL) process with a defined Sentiment Ontology Tree (SOT). The empirical analysis against a human- labeled data set demonstrates promising and reasonable performance of the pro- posed HL-SOT approach. While this pa- per is mainly on sentiment analysis on re- views of one product, our proposed HL- SOT approach is easily generalized to la- beling a mix of reviews of more than one products. 1 Introduction As the internet reaches almost every corner of this world, more and more people write reviews and share opinions on the World Wide Web. The user- generated opinion-rich reviews will not only help other users make better judgements but they are also useful resources for manufacturers of prod- ucts to keep track and manage customer opinions. However, as the number of product reviews grows, it becomes difficult for a user to manually learn the panorama of an interesting topic from existing online information. Faced with this problem, re- search works, e.g., (Hu and Liu, 2004; Liu et al., 2005; Lu et al., 2009), of sentiment analysis on product reviews were proposed and have become a popular research topic at the crossroads of infor- mation retrieval and computational linguistics. Carrying out sentiment analysis on product re- views is not a trivial task. Although there have al- ready been a lot of publications investigating on similar issues, among which the representatives are (Turney, 2002; Dave et al., 2003; Hu and Liu, 2004; Liu et al., 2005; Popescu and Etzioni, 2005; Zhuang et al., 2006; Lu and Zhai, 2008; Titov and McDonald, 2008; Zhou and Chaovalit, 2008; Lu et al., 2009), there is still room for improvement on tackling this problem. When we look into the de- tails of each example of product reviews, we find that there are some intrinsic properties that exist- ing previous works have not addressed in much de- tail. First of all, product reviews constitute domain- specific knowledge. The product’s attributes men- tioned in reviews might have some relationships between each other. For example, for a digital camera, comments on image quality are usually mentioned. However, a sentence like “40D han- dles noise very well up to ISO 800”, also refers to image …

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