Measuring opinion relevance in latent topic space

Wei Cheng, Xiaochuan Ni, Jian Tao Sun, Xiaoming Jin, Hye Chung Kum, Xiang Zhang, Wei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Opinion retrieval engines aim to retrieve documents containing user opinions towards a given search query. Different from traditional IR engines which rank documents by their topic relevance to the search query, opinion retrieval engines also consider opinion relevance. The result documents should contain user opinions which should be relevant to the search query. In previous opinion retrieval algorithms, opinion relevance scores are usually calculated by using very straightforward approaches, e.g., the distance between search query and opinion-carrying words. These approaches may cause two problems: 1) opinions in the returned result documents are irrelevant to the search query; 2) opinions related to the search query are not well identified. In this paper, we propose a new approach to deal with this topicopinion mismatch problem. We leverage the idea of Probabilistic Latent Semantic Analysis. Both queries and documents are represented in a latent topic space, and then opinion relevance is calculated semantically in this topic space. Experiments on the TREC blog datasets indicate that our approach is effective in measuring opinion relevance and the opinion retrieval system based on our algorithm yields significant improvements compared with most state-of-the-art methods.

Original languageEnglish (US)
Title of host publicationProceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011
Pages323-330
Number of pages8
DOIs
StatePublished - Dec 1 2011
Event2011 IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2011 and 2011 IEEE International Conference on Social Computing, SocialCom 2011 - Boston, MA, United States
Duration: Oct 9 2011Oct 11 2011

Publication series

NameProceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011

Other

Other2011 IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2011 and 2011 IEEE International Conference on Social Computing, SocialCom 2011
CountryUnited States
CityBoston, MA
Period10/9/1110/11/11

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Cheng, W., Ni, X., Sun, J. T., Jin, X., Kum, H. C., Zhang, X., & Wang, W. (2011). Measuring opinion relevance in latent topic space. In Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011 (pp. 323-330). [6113131] (Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011). https://doi.org/10.1109/PASSAT/SocialCom.2011.45