Automatic Construction of an Opinion-Term Vocabulary for Ad Hoc Retrieval

  • Giambattista Amati
  • Edgardo Ambrosi
  • Marco Bianchi
  • Carlo Gaibisso
  • Giorgio Gambosi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4956)


We present a method to automatically generate a term-opinion lexicon. We also weight these lexicon terms and use them at real time to boost the ranking with opinionated-content documents. We define very simple models both for opinion-term extraction and document ranking. Both the lexicon model and retrieval model are assessed. To evaluate the quality of the lexicon we compare performance with a well-established manually generated opinion-term dictionary. We evaluate the effectiveness of the term-opinion lexicon using the opinion task evaluation data of the TREC 2007 blog track.


Relevant Document Query Expansion Computational Linguistics Opinion Score Automatic Construction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Alias-i. Lingpipe named entity tagger,
  2. 2.
    Amati, G.: Frequentist and Bayesian approach to Information Retrieval. In: Lalmas, M., MacFarlane, A., Rüger, S.M., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds.) ECIR 2006. LNCS, vol. 3936, pp. 13–24. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Amati, G., Carpineto, C., Romano, G.: Query difficulty, robustness, and selective application of query expansion. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 127–137. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Amati, G., Carpineto, C., Romano, G.: Merging xml indices. In: Fuhr, N., Lalmas, M., Malik, S., Szlávik, Z. (eds.) INEX 2004. LNCS, vol. 3493, pp. 253–260. Springer, Heidelberg (2005)Google Scholar
  5. 5.
    Biemann, C., Heyer, G., Quasthoff, U., Richter, M.: The leipzig corpora collection - monolingual corpora of standard size. In: Proceedings of Corpus Linguistic 2007, Birmingham, UK (2007)Google Scholar
  6. 6.
    Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. In: Proceedings of the 27th. Annual Meeting of the Association for Computational Linguistics, pp. 76–83. Association for Computational Linguistics, Vancouver, B.C (1989)CrossRefGoogle Scholar
  7. 7.
    Eguchi, K., Lavrenko, V.: Sentiment retrieval using generative models. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, July 2006, pp. 345–354. Association for Computational Linguistics, Sydney, Australia (2006)Google Scholar
  8. 8.
    Esuli, A., Sebastiani, F.: SentiWordNet: A publicly available lexical resource for opinion mining. In: Proceedings of LREC-06, the 5th Conference on Language Resources and Evaluation (2006)Google Scholar
  9. 9.
    Fano, R.M.: Transmission of Information: A Statistical Theory of Communications. MIT Press, Cambridge, Wiley, New York (1961)Google Scholar
  10. 10.
    Hatzivassiloglou, V., McKeown, K.: Predicting the semantic orientation of adjectives. In: acl97, pp. 174–181 (1997)Google Scholar
  11. 11.
    Manmatha, R., Rath, T., Feng, F.: Modeling score distributions for combining the outputs of search engines. In: SIGIR 2001: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 267–275. ACM, New York (2001)CrossRefGoogle Scholar
  12. 12.
    Mishne, G.: Multiple ranking strategies for opinion retrieval in blogs. In: The Fifteenth Text REtrieval Conference (TREC 2006) Proceedings (2006)Google Scholar
  13. 13.
    Ounis, I., Amati, G., Plachouras, V., He, B., Macdonald, C., Lioma, C.: Terrier: A High Performance and Scalable Information Retrieval Platform. In: Proceedings of ACM SIGIR’06 Workshop on Open Source Information Retrieval (OSIR 2006) (2006)Google Scholar
  14. 14.
    Ounis, I., de Rijke, M., Macdonald, C., Mishne, G., Soboroff, I.: Overview of the trec-2006 blog track. In: Proceedings of the Text REtrieval Conference (TREC 2006), National Institute of Standards and Technology (2006)Google Scholar
  15. 15.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: EMNLP 2002: Proceedings of the ACL-02 conference on Empirical methods in natural language processing, pp. 79–86. Association for Computational Linguistics, Morristown, NJ, USA (2002)CrossRefGoogle Scholar
  16. 16.
    Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 conference on Empirical methods in natural language processing, pp. 105–112. Association for Computational Linguistics, Morristown, NJ, USA (2003)CrossRefGoogle Scholar
  17. 17.
    Skomorowski, J., Vechtomova, O.: Ad hoc retrieval of documents with topical opinion. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECIR 2007. LNCS, vol. 4425, pp. 405–417. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: acl2002, pp. 417–424 (2002)Google Scholar
  19. 19.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of HLT-EMNLP (2005)Google Scholar
  20. 20.
    Xu, J., Croft, W.B.: Query expansion using local and global document analysis. In: Proceedings of ACM SIGIR, Zurich, Switzerland, August 1996, pp. 4–11 (1996)Google Scholar
  21. 21.
    Zhang, W., Yu, C.: Uic at trec 2006 blog track. In: The Fifteenth Text REtrieval Conference (TREC 2006) Proceedings (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Giambattista Amati
    • 1
  • Edgardo Ambrosi
    • 2
  • Marco Bianchi
    • 2
  • Carlo Gaibisso
    • 2
  • Giorgio Gambosi
    • 3
  1. 1.Fondazione Ugo BordoniRomeItaly
  2. 2.IASI “Antonio Ruberti” - CNRRomeItaly
  3. 3.University of Tor VergataRomeItaly

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