High precision opinion retrieval using sentiment-relevance flows

Seung Wook Lee, Jung Tae Lee, Young In Song, Hae Chang Rim

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

6 Citations (Scopus)

Abstract

Opinion retrieval involves the measuring of opinion score of a document about the given topic. We propose a new method, namely sentiment-relevance flow, that naturally unifies the topic relevance and the opinionated nature of a document. Experiments conducted over a large-scaled Web corpus show that the proposed approach improves performance of opinion retrieval in terms of precision at top ranks.

Original languageEnglish
Title of host publicationSIGIR 2010 Proceedings - 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages817-818
Number of pages2
DOIs
Publication statusPublished - 2010
Event33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010 - Geneva, Switzerland
Duration: 2010 Jul 192010 Jul 23

Publication series

NameSIGIR 2010 Proceedings - 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010
CountrySwitzerland
CityGeneva
Period10/7/1910/7/23

Keywords

  • Opinion retrieval
  • Sentiment analysis
  • Sentiment-relevance flow

ASJC Scopus subject areas

  • Information Systems

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  • Cite this

    Lee, S. W., Lee, J. T., Song, Y. I., & Rim, H. C. (2010). High precision opinion retrieval using sentiment-relevance flows. In SIGIR 2010 Proceedings - 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 817-818). (SIGIR 2010 Proceedings - 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval). https://doi.org/10.1145/1835449.1835631