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ABSTRACT

With the development of Web 2.0, the web has become an extremely valuable source for mining opinions. In this paper, we study how to automatically mine product features and opinions by integrating multiple review sources. We propose an integration strategy to solve the problem. Experiments show that the proposed strategy is effective.

REFERENCES

• 1: 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]
• 2: 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]
• 3: Jay J. Jiang and David W. Conrath. 1997. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of International Conference on Research in Computational Linguistics, Taiwan.
• 4: Avigdor Gal , Giovanni Modica , Hasan Jamil , Ami Eyal, Automatic ontology matching using application semantics, AI Magazine, v.26 n.1, p.21-31, March 2005
• 5: Peter D. Turney, Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL, Proceedings of the 12th European Conference on Machine Learning, p.491-502, September 05-07, 2001

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:
clustering, information extraction, opinion integration