FREQUENCY OF SENTENTIAL CONTEXTS VS. FREQUENCY OF QUERY TERMS IN OPINION RETRIEVAL

Sylvester Olubolu Orimaye, Saadat M. Alhashmi, Siew Eu-Gene

2011

Abstract

Many opinion retrieval techniques use frequency of query terms as a measurement for detecting documents that contain opinion. However, using frequency of query terms leads to bias in context-dependent opinion retrieval such that all documents containing query terms are retrieved, regardless of contextual relevance to the intent of the human seeking the opinion. This can be described as non-contextual relevance problem in opinion retrieval systems such as Google Blogs Search and Technorati Blog Directory. Sentence-level contextual understanding and grammatical dependencies need be considered to ensure documents retrieved contain large proportion of textual contents that have the same underlying meaning with the given query instead of using frequency of individual query terms. Thus, we present specific challenges with state-of-the-art opinion retrieval techniques that rely on frequency of query terms and we propose a grammar-based technique for efficient context-dependent opinion retrieval. We believe our proposed technique can solve the non-contextual relevance problem common to opinion retrieval systems, and can be used for context-dependent retrieval such as expert search systems, faceted-opinion retrieval, opinion trend analytic, and personalized web search.

References

  1. Bermingham, A. and Smeaton, A.F. (2009). A study of inter-annotator agreement for opinion retrieval. In SIGIR'09: Proceedings of the 32nd international conference on Research and development in information retrieval, pages 784-785. ACM.
  2. Bermingham, A. and Smeaton, A.F. (2009). A study of inter-annotator agreement for opinion retrieval. In SIGIR'09: Proceedings of the 32nd international conference on Research and development in information retrieval, pages 784-785. ACM.
  3. Bruce, E. et al., (2009) Mapping the Arabic blogosphere: politics, culture and dissent. Berkman Center for Internet and Society at Harvard University.
  4. Bruce, E. et al., (2009) Mapping the Arabic blogosphere: politics, culture and dissent. Berkman Center for Internet and Society at Harvard University.
  5. Ding, X., Liu, B. and Yu, P.S. (2008). A holistic lexiconbased approach to opinion mining. In Proceedings of the international conference on Web search and web data mining. Pages 231-240.ACM.
  6. Ding, X., Liu, B. and Yu, P.S. (2008). A holistic lexiconbased approach to opinion mining. In Proceedings of the international conference on Web search and web data mining. Pages 231-240.ACM.
  7. Hannah, D., Macdonald, C., Peng, J., He, B., Ounis, I. (2007). University of Glasgow at TREC 2007: Experiments in Blog and Enterprise Tracks with Terrier. In TREC, 2007.
  8. Hannah, D., Macdonald, C., Peng, J., He, B., Ounis, I. (2007). University of Glasgow at TREC 2007: Experiments in Blog and Enterprise Tracks with Terrier. In TREC, 2007.
  9. Baldridge , J.M., Kruijiff, G-J.M. (2004). Course Notes on Combinatory Categorial Grammar.
  10. Baldridge , J.M., Kruijiff, G-J.M. (2004). Course Notes on Combinatory Categorial Grammar.
  11. Kim, J., Li, J-J. and Lee, J-H. (2010). Evaluating multilanguage-comparability of subjectivity analysis systems. In ACL'10: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 595-603.
  12. Kim, J., Li, J-J. and Lee, J-H. (2010). Evaluating multilanguage-comparability of subjectivity analysis systems. In ACL'10: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 595-603.
  13. Krahmer, E. (2010) What Computational Linguists Can Learn from Psychologists. Association for Computational Linguistics, 36(2): 285-294.
  14. Krahmer, E. (2010) What Computational Linguists Can Learn from Psychologists. Association for Computational Linguistics, 36(2): 285-294.
  15. Liu, B. (2010) Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing, Second Edition.
  16. Liu, B. (2010) Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing, Second Edition.
  17. Sarmento, L., Carvalho, P., Silva, J.M., Oliveira, E. (2009) Automatic creation of a reference corpus for political opinion mining in user-generated content. In CIKM 7809: Proceeding of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion, pages 29-36. ACM.
  18. Sarmento, L., Carvalho, P., Silva, J.M., Oliveira, E. (2009) Automatic creation of a reference corpus for political opinion mining in user-generated content. In CIKM 7809: Proceeding of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion, pages 29-36. ACM.
  19. Lv, Y. and Zhai, C. (2009). Positional language models for information retrieval. In SIGIR'09: Proceedings of the 32nd international conference on Research and development in information retrieval, pages 299-306. ACM.
  20. Lv, Y. and Zhai, C. (2009). Positional language models for information retrieval. In SIGIR'09: Proceedings of the 32nd international conference on Research and development in information retrieval, pages 299-306. ACM.
  21. Pang, B. and Lee, L. (2008). Opinion Mining and Sentiment Analysis. In Found. Trends Inf. Retr.,2(1- 2): 1-135.
  22. Pang, B. and Lee, L. (2008). Opinion Mining and Sentiment Analysis. In Found. Trends Inf. Retr.,2(1- 2): 1-135.
  23. Zhai,C. (Statistical Language Models for Information Retrieval: A Critical Review. In Found. Trends Inf. Retr., 2(3):137-213.
  24. Zhai,C. (Statistical Language Models for Information Retrieval: A Critical Review. In Found. Trends Inf. Retr., 2(3):137-213.
  25. Gerani, S., Carman, M.J. Crestani, F. (2010). ProximityBased Opinion Retrieval. In SIGIR, page 978. ACM.
  26. Gerani, S., Carman, M.J. Crestani, F. (2010). ProximityBased Opinion Retrieval. In SIGIR, page 978. ACM.
  27. Siersdorfer, S., Chelaru, S. and Pedro, J.S. (2010). How Useful are Your Comments? Analyzing and Predicting YouTube Comments and Comment Ratings. In International World Wide Web Conference, pages 891-900.
  28. Siersdorfer, S., Chelaru, S. and Pedro, J.S. (2010). How Useful are Your Comments? Analyzing and Predicting YouTube Comments and Comment Ratings. In International World Wide Web Conference, pages 891-900.
  29. Wei, Z. and Clement, Y. (2006). UIC at TREC 2006 Blog Track, In TREC, 2006.
  30. Wei, Z. and Clement, Y. (2006). UIC at TREC 2006 Blog Track, In TREC, 2006.
Download


Paper Citation


in Harvard Style

Olubolu Orimaye S., M. Alhashmi S. and Eu-Gene S. (2011). FREQUENCY OF SENTENTIAL CONTEXTS VS. FREQUENCY OF QUERY TERMS IN OPINION RETRIEVAL . In Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-8425-51-5, pages 607-610. DOI: 10.5220/0003401206070610


in Harvard Style

Olubolu Orimaye S., M. Alhashmi S. and Eu-Gene S. (2011). FREQUENCY OF SENTENTIAL CONTEXTS VS. FREQUENCY OF QUERY TERMS IN OPINION RETRIEVAL . In Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-8425-51-5, pages 607-610. DOI: 10.5220/0003401206070610


in Bibtex Style

@conference{webist11,
author={Sylvester Olubolu Orimaye and Saadat M. Alhashmi and Siew Eu-Gene},
title={FREQUENCY OF SENTENTIAL CONTEXTS VS. FREQUENCY OF QUERY TERMS IN OPINION RETRIEVAL},
booktitle={Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2011},
pages={607-610},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003401206070610},
isbn={978-989-8425-51-5},
}


in Bibtex Style

@conference{webist11,
author={Sylvester Olubolu Orimaye and Saadat M. Alhashmi and Siew Eu-Gene},
title={FREQUENCY OF SENTENTIAL CONTEXTS VS. FREQUENCY OF QUERY TERMS IN OPINION RETRIEVAL},
booktitle={Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2011},
pages={607-610},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003401206070610},
isbn={978-989-8425-51-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - FREQUENCY OF SENTENTIAL CONTEXTS VS. FREQUENCY OF QUERY TERMS IN OPINION RETRIEVAL
SN - 978-989-8425-51-5
AU - Olubolu Orimaye S.
AU - M. Alhashmi S.
AU - Eu-Gene S.
PY - 2011
SP - 607
EP - 610
DO - 10.5220/0003401206070610


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - FREQUENCY OF SENTENTIAL CONTEXTS VS. FREQUENCY OF QUERY TERMS IN OPINION RETRIEVAL
SN - 978-989-8425-51-5
AU - Olubolu Orimaye S.
AU - M. Alhashmi S.
AU - Eu-Gene S.
PY - 2011
SP - 607
EP - 610
DO - 10.5220/0003401206070610