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Sentiment Analysis

Japanese | English

Overview

A technique to detect favorable and unfavorable opinions toward specific subjects (such as organizations and their products) within large numbers of documents offers enormous opportunities for various applications. It would provide powerful functionality for competitive analysis, marketing analysis, and detection of unfavorable rumors for risk management.

Our sentiment analysis approach is to extract sentiments associated with polarities of positive or negative for specific subjects from a document, instead of classifying the whole document into positive or negative. The essential issues in sentiment analysis are to identify how sentiments are expressed in texts and whether the expressions indicate positive (favorable) or negative (unfavorable) opinions toward the subject. In order to improve the accuracy of the sentiment analysis, it is important to properly identify the semantic relationships between the sentiment expressions and the subject. By applying semantic analysis with a syntactic parser and sentiment lexicon, our prototype system achieved high precision (75-95%, depending on the data) in finding sentiments within Web pages and news articles.

Publications

International Conference Paper

  • Kanayama, H. and Nasukawa, T.: "Textual Demand Analysis: detection of users' wants and needs from opinions", The 22nd International Conference on Computational Linguistics (COLING 2008), pp. 409--416, August 2008
  • Kanayama, H. and Nasukawa, T.: "Fully Automatic Lexicon Expanding for Domain-oriented Sentiment Analysis" , EMNLP: Empirical Methods in Natural Language Processing, pp, 355--363, July 2006
  • Kanayama, H., Nasukawa, T and Watanabe, H.: "Deeper Sentiment Analysis Using Machine Translation Technology", The 20th International Conference on Computational Linguistics (COLING 2004), pp. 494--500, August 2004.
  • Yi, J., Nasukawa, T., Bunescu, R. and Niblack, W.: "Sentiment Analyzer: Extracting Sentiments About A Given Topic Using Natural Language Processing Techniques", The Third IEEE International Conference on Data Mining, pp. 427--434, November 2003.
  • Nasukawa, T. and Yi, J.: "Sentiment Analysis: Capturing Favorability Using Natural Language Processing", Second International Conference on Knowledge Capture, pp. 70--77, October 2003.

Domestic Conference Paper

  • Nasukawa, T., Kanayama, H., Tsuboi, Y. and Watanabe, H.: "Natural Language Processing by Using Favorable/Unfavorable Context", The 11th Annual Meeting of The Association for NLP,(in Japanese). pp. 153--156 (Mar. 2005)
  • Kanayama, H. and Nasukawa, T.: "Extracting and Grouping of Request Expressions", The 11th Annual Meeting of The Association for NLP (in Japanese). pp. 660--663 (Mar. 2005)
  • Nasukawa, T. and Kanayama, H.: "Acquisition of Sentiment Lexicon by Using Context Coherence", IPSJ SIG-NL-162 (in Japanese). pp. 109--116 (Jul. 2004)