Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

This paper presents the SELC Model (SElf-Supervised, (Lexicon-based and (Corpus-based Model) for sentiment classification. The SELC Model includes two phases. The first phase is a lexicon-based iterative process. In this phase, some reviews are initially classified based on a sentiment dictionary. Then more reviews are classified through an iterative process with a negative/positive ratio control. In the second phase, a supervised classifier is learned by taking some reviews classified in the first phase as training data. Then the supervised classifier applies on other reviews to revise the results produced in the first phase. Experiments show the effectiveness of the proposed model. SELC totally achieves 6.63% F₁-score improvement over the best result in previous studies on the same data (from 82.72% to 89.35%). The first phase of the SELC Model independently achieves 5.90% improvement (from 82.72% to 88.62%). Moreover, the standard deviation of F₁-scores is reduced, which shows that the SELC Model could be more suitable for domain-independent sentiment classification.

REFERENCES

• 1: Alina Andreevskaia and Sabine Bergler. 2008. When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, pages 290--298.
• 2: Anthony Aue and Michael Gamon. 2005. Customizing sentiment classifiers to new domains: a case study. In Proceedings of the International Conference on Recent Advances in Natural Language Processing, Borovets, BG.
• 3: Bo Pang , Lillian Lee, A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts, Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, p.271-es, July 21-26, 2004, Barcelona, Spain [doi>10.3115/1218955.1218990]
• 4: Bo Pang , Lillian Lee , Shivakumar Vaithyanathan, Thumbs up?: sentiment classification using machine learning techniques, Proceedings of the ACL-02 conference on Empirical methods in natural language processing, p.79-86, July 06, 2002 [doi>10.3115/1118693.1118704]
• 5: Ethem Alpaydin, Introduction to Machine Learning (Adaptive Computation and Machine Learning), The MIT Press, 2004
• 6: Hong Yu , Vasileios Hatzivassiloglou, Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences, Proceedings of the 2003 conference on Empirical methods in natural language processing, p.129-136, July 11, 2003 [doi>10.3115/1119355.1119372]
• 7: Hang Cui , Vibhu Mittal , Mayur Datar, Comparative experiments on sentiment classification for online product reviews, proceedings of the 21st national conference on Artificial intelligence, p.1265-1270, July 16-20, 2006, Boston, Massachusetts
• 8: J. C. Na, H. Sui, C. Khoo, S. Chan,&Y. Zhou. 2004. Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews. In I. C. McIlwaine (Ed.), Knowledge Organization and the Global Information Society: Proceedings of the Eighth International ISKO Conference (pages 49--54). Wurzburg, Germany: Ergon Verlag.
• 9: Jonathon Read, Using emoticons to reduce dependency in machine learning techniques for sentiment classification, Proceedings of the ACL Student Research Workshop, June 27-27, 2005, Ann Arbor, Michigan
• 10: Kushal Dave , Steve Lawrence , David M. Pennock, Mining the peanut gallery: opinion extraction and semantic classification of product reviews, Proceedings of the 12th international conference on World Wide Web, May 20-24, 2003, Budapest, Hungary [doi>10.1145/775152.775226]
• 11: Mark Drezde, John Blitzer, and Fernando Pereira. 2007. Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, Prague, CZ.
• 12: Michael Gamon , Anthony Aue, Automatic identification of sentiment vocabulary: exploiting low association with known sentiment terms, Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing, June 29-29, 2005, Ann Arbor, Michigan
• 13: Peter D. Turney, Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, July 07-12, 2002, Philadelphia, Pennsylvania [doi>10.3115/1073083.1073153]
• 14: Songbo Tan , Gaowei Wu , Huifeng Tang , Xueqi Cheng, A novel scheme for domain-transfer problem in the context of sentiment analysis, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, November 06-10, 2007, Lisbon, Portugal [doi>10.1145/1321440.1321590]
• 15: Taras Zagibalov and John Carroll. 2008a. Unsupervised Classification of Sentiment and Objectivity in Chinese Text. In Proceedings of the Third International Joint Conference on Natural Language Processing. 304--311.
• 16: Taras Zagibalov , John Carroll, Automatic seed word selection for unsupervised sentiment classification of Chinese text, Proceedings of the 22nd International Conference on Computational Linguistics, p.1073-1080, August 18-22, 2008, Manchester, United Kingdom

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing

General Terms:
Algorithms, Experimentation, Performance

Keywords:
information retrieval, opinion mining, sentiment classification