Semantic Approaches to Fine and Coarse-Grained Feature-Based Opinion Mining

  • Alexandra Balahur
  • Andrés Montoyo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5723)


Feature-based opinion mining from product reviews is a difficult task, both due to the high semantic variability of opinion expression, as well as because of the diversity of characteristics and sub-characteristics describing the products and the multitude of opinion words used to depict them. Further on, this task supposes not only the discovery of directly expressed opinions, but also the extraction of phrases that indirectly or implicitly value objects and their characteristics, by means of emotions or attitudes. Last, but not least, evaluation of results is difficult, because there is no standard corpus available that is annotated at such a fine-grained level and no annotation scheme defined for this purpose. This article presents our contributions to this task, given by the definition and application of an opinion annotation scheme, the testing of different methodologies to detect phrases related to different characteristics and the employment of Textual Entailment recognition for opinion mining. Finally, we test our approaches both on the built corpus, as well as on an ad-hoc built collection of reviews that we evaluate on the basis of the stars given. We prove that our approaches are appropriate and give high precision results.


opinion mining emotion detection polarity classification Textual Entailment Recognition 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Strapparava, C., Valitutti, A.: WordNet-Affect: an affective extension of WordNet. In: Proceedings of the 4th International Conference on Language (2004)Google Scholar
  2. 2.
    Esuli, A., Sebastiani, F.: SentiWordNet: A Publicly Available Resource for Opinion Mining. In: Proceedings of the 6th International Conference on Language Resources and Evaluation, LREC 2006, Genoa, Italy (2006)Google Scholar
  3. 3.
    Cerini, S., Compagnoni, V., Demontis, A., Formentelli, M., Gandini, G.: Micro-WNOp: A gold standard for the evaluation of automatically compiled lexical resources for opinion mining. In: Angeli, F. (ed.) Language resources and linguistic theory: Typology, second language acquisition, English linguistics, Milano, Italy (2007)Google Scholar
  4. 4.
    Balahur, A., Montoyo, A.: Applying a Culture Dependent Emotion Triggers Database for Text Valence and Emotion Classification. In: Proceedings of the Symposium on Affective Language in Human and Machine, Aberdeen, Scotland (2008)Google Scholar
  5. 5.
    Wiebe, J.M.: Tracking point of view in narrative. Computational Linguistics 20, 233–287 (1994)Google Scholar
  6. 6.
    Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Language Resources and Evaluation 39(2-3), 165–210 (2005)CrossRefGoogle Scholar
  7. 7.
    Riloff, E., Wiebe, J., Phillips, W.: Exploiting Subjectivity Classification to Improve Information Extraction. In: Proceedings of the 20th National Conference on Artificial Intelligence, AAAI 2005 (2005)Google Scholar
  8. 8.
    Stoyanov, V., Cardie, C.: Toward Opinion Summarization: Linking the Sources. In: COLING-ACL 2006 Workshop on Sentiment and Subjectivity in Text (2006)Google Scholar
  9. 9.
    Turney, P.: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: Proceedings of ACL 2002, pp. 417–424 (2002)Google Scholar
  10. 10.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of EMNLP 2002, the Conference on Empirical Methods in Natural Language Processing (2002)Google Scholar
  11. 11.
    Dave, K., Lawrence, S., Pennock, D.: Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews. In: Proceedings of WWW 2003 (2003)Google Scholar
  12. 12.
    Mullen, T., Collier, N.: Sentiment Analysis Using Support Vector Machines with Diverse Information Sources. In: Proceedings of EMNLP (2004)Google Scholar
  13. 13.
    Chaovalit, P., Zhou, L.: Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches. In: Proceedings of HICSS 2005, the 38th Hawaii International Conference on System Sciences (2005)Google Scholar
  14. 14.
    Goldberg, A.B., Zhu, J.: Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization. In: HLT-NAACL Workshop on Textgraphs: Graph-based Algorithms for Natural Language Processing (2006)Google Scholar
  15. 15.
    Ng, V., Dasgupta, S., Arifin, S.M.: Examining the Role of Linguistic Knowledge Sources in the Identification and Classification of Reviews. In: Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions (2006)Google Scholar
  16. 16.
    Gamon, M., Aue, S., Corston-Oliver, S., Ringger, E.: Mining Customer Opinions from Free Text. LNCS. Springer, Heidelberg (2005)Google Scholar
  17. 17.
    Cui, H., Mittal, V., Datar, M.: Comparative Experiments on Sentiment Classification for Online Product Reviews. In: Proceedings of the 21st National Conference on Artificial Intelligence AAAI (2006)Google Scholar
  18. 18.
    Riloff, E., Wiebe, J.: Learning Extraction Patterns for Subjective Expressions. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing (2003)Google Scholar
  19. 19.
    Hatzivassiloglou, V., Wiebe, J.: Effects of adjective orientation and gradability on sentence subjectivity. In: Proceedings of COLING (2000)Google Scholar
  20. 20.
    Wiebe, J., Riloff, E.: Creating Subjective and Objective Sentence Classifiers from Unannotated Texts. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 486–497. Springer, Heidelberg (2005)Google Scholar
  21. 21.
    Wilson, T., Wiebe, J., Hwa, R.: Just how mad are you? Finding strong and weak opinion clauses. In: Proceedings of AAAI (2004)Google Scholar
  22. 22.
    Kim, S.M., Hovy, E.: Determining the Sentiment of Opinions. In: Proceedings of COLING (2004)Google Scholar
  23. 23.
    Lin, W.H., Wilson, T., Wiebe, J., Hauptman, A.: Which Side are You On? Identifying Perspectives at the Document and Sentence Levels. In: Proceedings of the Tenth Conference on Natural Language Learning CoNLL (2006)Google Scholar
  24. 24.
    Turney, P., Littman, M.: Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems 21 (2003)Google Scholar
  25. 25.
    Stoyanov, V., Cardie, C., Litman, D., Wiebe, J.: Evaluating an Opinion Annotation Scheme Using a New Multi-Perspective Question and Answer Corpus. In: AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and ApplicationsGoogle Scholar
  26. 26.
    Hu, M., Liu, B.: Mining Opinion Features in Customer Reviews. In: Proceedings of Nineteenth National Conference on Artificial Intelligence AAAI (2004)Google Scholar
  27. 27.
    Ding, X., Liu, B., Yu, P.: A Holistic Lexicon-Based Approach to Opinion Mining. In: Proceedings of First ACM International Conference on Web Search and Data Mining (WSDM 2008), Stanford, California, USA (2008)Google Scholar
  28. 28.
    Dagan, I., Glickman, O., Magnini, B.: The PASCAL Recognising Textual Entailment Challenge. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 177–190. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  29. 29.
    Iftene, A., Balahur-Dobrescu, A.: Hypothesis transformation and semantic variability rules for recognizing textual entailment. In: Proceedings of the ACL Workshop on Entailment and Paraphrasis, ACL (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alexandra Balahur
    • 1
  • Andrés Montoyo
    • 1
  1. 1.Department of Software and Computing SystemsUniversity of AlicanteAlicanteSpain

Personalised recommendations