Using Anaphora Resolution to Improve Opinion Target Identification in Movie Reviews

Current work on automatic opinion mining has ignored opinion targets expressed by anaphorical pronouns, thereby missing a significant number of opinion targets. In this paper we empirically evaluate whether using an off-the-shelf anaphora resolution algorithm can improve the performance of a baseline opinion mining system. We present an analysis based on two different anaphora resolution systems. Our experiments on a movie review corpus demonstrate, that an unsupervised anaphora resolution algorithm significantly improves the opinion target extraction. We furthermore suggest domain and task specific extensions to an off-the-shelf algorithm which in turn yield significant improvements.

[1]  Breck Baldwin,et al.  CogNIAC: high precision coreference with limited knowledge and linguistic resources , 1997 .

[2]  Ruslan Mitkov,et al.  Robust Pronoun Resolution with Limited Knowledge , 1998, ACL.

[3]  José Luis Vicedo González,et al.  Applying Anaphora Resolution to Question Answering and Information Retrieval Systems , 2000, Web-Age Information Management.

[4]  José L. Vicedo,et al.  Applying Anaphora Resolution to Question Answering and Information Retrieval Systems , 2000 .

[5]  Peter D. Turney Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[6]  Razvan C. Bunescu,et al.  Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques , 2003, Third IEEE International Conference on Data Mining.

[7]  Jeonghee Yi,et al.  Sentiment analysis: capturing favorability using natural language processing , 2003, K-CAP '03.

[8]  Massimo Poesio,et al.  A General-Purpose, Off-the-shelf Anaphora Resolution Module: Implementation and Preliminary Evaluation , 2004, LREC.

[9]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[10]  Christopher D. Manning,et al.  Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.

[11]  Bo Pang,et al.  Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales , 2005, ACL.

[12]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[13]  Xiaoyan Zhu,et al.  Movie review mining and summarization , 2006, CIKM '06.

[14]  Eduard Hovy,et al.  Extracting Opinions, Opinion Holders, and Topics Expressed in Online News Media Text , 2006 .

[15]  Claire Cardie,et al.  Topic Identification for Fine-Grained Opinion Analysis , 2008, COLING.

[16]  Micha Elsner,et al.  EM Works for Pronoun Anaphora Resolution , 2009, EACL.

[17]  Nicolas Nicolov,et al.  Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations , 2009, ICWSM.