The utility of linguistic rules in opinion mining

ABSTRACT

Online product reviews are one of the important opinion sources on the Web. This paper studies the problem of determining the semantic orientations (positive or negative) of opinions expressed on product features in reviews. Most existing approaches use a set of opinion words for the purpose. However, the semantic orientations of many words are context dependent. In this paper, we propose to use some linguistic rules to deal with the problem together with a new opinion aggregation function. Extensive experiments show that these rules and the function are highly effective. A system, called Opinion Observer, has also been built.

REFERENCES

• 1: Vasileios Hatzivassiloglou , Kathleen R. McKeown, Predicting the semantic orientation of adjectives, Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics, p.174-181, July 07-12, 1997, Madrid, Spain
• 2: Minqing Hu , Bing Liu, Mining and summarizing customer reviews, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014073]
• 3: H. Kanayama and T. Nasukawa. Fully Automatic Lexicon Expansion for Domain-Oriented Sentiment Analysis. EMNLP-06, 2006.
• 4: Bing Liu, Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications), Springer-Verlag New York, Inc., Secaucus, NJ, 2006
• 5: Ana-Maria Popescu , Oren Etzioni, Extracting product features and opinions from reviews, Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, p.339-346, October 06-08, 2005, Vancouver, British Columbia, Canada [doi>10.3115/1220575.1220618]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

General Terms:
Algorithms, Experimentation

Keywords:
opinion mining, sentiment analysis