Sentiment-based search in digital libraries

ABSTRACT

Several researchers have developed tools for classifying/ clustering Web search results into different topic areas (such as sports, movies, travel, etc.), and to help users identify relevant results quickly in the area of interest. This study follows a similar approach, but is in the area of sentiment classification -- automatically classifying on-line review documents according to the overall sentiment expressed in them. This paper presents a prototype system that has been developed to perform sentiment categorization of Web search results. It assists users to quickly focus on recommended (or non-recommended) information by classifying Web search results into four categories: positive, negative, neutral, and non-review documents, by using an automatic classifier based on a supervised machine learning algorithm, Support Vector Machine (SVM).

REFERENCES

• 1: Hua-Jun Zeng , Qi-Cai He , Zheng Chen , Wei-Ying Ma , Jinwen Ma, Learning to cluster web search results, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009030]
• 2: C. R. Palmer , J. Pesenti , R. E. Valdes-Perez , M. G. Christel , A. G. Hauptmann , D. Ng , H. D. Wactlar, Demonstration of hierarchical document clustering of digital library retrieval results, Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries, p.451, January 2001, Roanoke, Virginia, United States [doi>10.1145/379437.379770]
• 3: Na, J.-C., Sui, H., Khoo, C., Chan, S., and Zhou, Y. Effectiveness of Simple Linguistic Processing in Automatic Sentiment Classification of Product Reviews, In Proceedings of the 8th International Society for Knowledge Organization Conference '2004, London, UK, July 2004, 49--54.
• 4: Vasileios Hatzivassiloglou , Janyce M. Wiebe, Effects of adjective orientation and gradability on sentence subjectivity, Proceedings of the 18th conference on Computational linguistics, p.299-305, July 31-August 04, 2000, Saarbrücken, Germany [doi>10.3115/990820.990864]

INDEX TERMS

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

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

General Terms:
Algorithms, Design, Theory

Keywords:
automatic text classification, digital libraries, sentiment classification