Skip to main content

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
Cancel
×
×
Home
  • Home
Article
  • Cited by 2
  • Cited by
    Crossref Citations
    Crossref logo
    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.
    Wijnhoven, Fons and Bloemen, Oscar 2014. External validity of sentiment mining reports: Can current methods identify demographic biases, event biases, and manipulation of reviews?. Decision Support Systems, Vol. 59, Issue. , p. 262.
    D. Munezero, Myriam Montero, Calkin Suero Sutinen, Erkki and Pajunen, John 2014. Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text. IEEE Transactions on Affective Computing, Vol. 5, Issue. 2, p. 101.
    ×
  • Get access
    Add to cart USD35.00
    Check if you have access via personal or institutional login
    Log in Register Recommend to librarian

Learning opinions in user-generated web content

  • M. SOKOLOVA (a1) and G. LAPALME (a2)
Abstract

The user-generated Web content has been intensively analyzed in Information Extraction and Natural Language Processing research. Web-posted reviews of consumer goods are studied to find customer opinions about the products. We hypothesize that nonemotionally charged descriptions can be applied to predict those opinions. The descriptions may include indicators of product size (tall), commonplace (some), frequency of happening (often), and reviewer certainty (maybe). We first construct patterns of how the descriptions are used in consumer-written texts and then represent individual reviews through these patterns. We propose a semantic hierarchy that organizes individual words into opinion types. We run machine learning algorithms on five data sets of user-written product reviews: four are used in classification experiments, another one for regression and classification. The obtained results support the use of non-emotional descriptions in opinion learning.

Copyright
COPYRIGHT: © Cambridge University Press 2011
References
Hide All
Andreevskaia, A., and Bergler, S. 2008. When specialists and generalists work together: overcoming domain dependence in sentiment tagging. In Proceedings of the ACL-08: HLT, pp. 290–98.
Bednarek, M. 2006. Evaluation in Media Discourse. New York, NY: Continuum.
Bednarek, M. 2009. Dimensions of evaluation. Cognitive and linguistic perspectives. Pragmatics & Cognition 17 (1): 146–75.
Benamara, F., Cesarano, C., Picariello, A., Reforgiato, D., and Subrahmanian, V. 2007. Sentiment analysis: adjectives and adverbs are better than the adjectives alone. In Proceedings of the International Conference on Weblogs and Social Media (ICWSM-2007).
Ben-David, S., Blitzer, J., Crammer, K., and Pereira, F. 2006. Analysis of representations for domain adaptation. In Proceedings of the Neural Information Processing Systems.
Bethard, S., Yu, H., Thornton, A., Hatzivassiloglou, V., and Jurafsky, D. 2004. Automatic extraction of opinion propositions and their holders. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications.
Biber, D., Johansson, S., Leech, G., Conrad, S., and Finegan, E. 1999. Longman Grammar of Spoken and Written English. Upper Saddle River, NJ: Longman.
Blitzer, J., Dredze, M., and Pereira, F. 2007. Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, Association for Computational Linguistics, pp. 440–47.
Bolinger, D. 1972. Degree Words. The Netherlands: Mouton De Gruyter.
Breck, E., Choi, Y., and Cardie, C. 2007. Identifying expressions of opinion in context. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI-2007), pp. 2683–88.
Crystal, D. 2006. Language and the Internet. Cambridge, UK: Cambridge University Press.
Dasgupta, S., and Ng, V. 2009. Mine the easy, classify the hard: a semi-supervised approach to automatic sentiment classification. In Proceedings of ACL 2009, Association for Computational Linguistics, pp. 701–709.
De Houwer, J. 2009. How do people evaluate objects? A brief review. Social and Personallity Psycology Compass 3 (1): 36–48.
Directorate for Science, Technology and Industry. 2007. Participative Web: User-Created Content, Committee for Information, Computer and Communication Policy. Working Party on the Information Economy.
Esuli, A. and Sebastiani, F. 2006a. SentiWordNet: a publicly available lexical resource for opinion mining. In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC-2006).
Esuli, A. and Sebastiani, F. 2006b. Determining term subjectivity and term orientation for opinion mining. In Proceedings of the European Chapter of the Association for Computational Linguistics (EACL-2006).
Esuli, A., and Sebastiani, F. 2007. Random-walk models of term semantics: an application to opinion-related properties. In Proceedings of the 3rd Language and Technology Conference (LTC-2003).
Feiguina, O., and Lapalme, G. 2007. Query-based summarization of customer reviews. In Proceedings of the 20th Canadian Conference on Artificial Intelligence (AI-2007), pp. 452–63, New Mexico: Springer.
Firth, J. et al. (eds). A synopsis of linguistic theory 1930–1955. In Studies in Linguistic Analysis, pp. 1–32. Oxford, UK: Basil Blackwell (for the Philological Society).
Hall, M., and Holmes, G. 2003. Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering 15 (6): 1437–47.
Halliday, M. 1994. An Introduction to Functional Grammar, 2nd ed.New York, NY: Edward Arnold.
Hine, M., Murphy, S., Weber, M., and Kersten, G. 2009. The role of emotion and language in dyadic E-negotiations. Group Decision and Negotiation 18: 193–211.
Hu, M., and Liu, B. 2004. Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD International Conf on Knowledge Discovery and Data Mining (KDD-2004), pp. 168–77.
Huddleston, R., and Pullum, G. 2002. The Cambridge Grammar of the English Language. Cambridge, UK: Cambridge University Press.
Hunston, S., and Francis, G. 2000. Pattern Grammar: A Corpus-Driven Approach to the Lexical Grammar of English. Philadelphia PA: John Benjamins.
Inui, K., Abe, S., Hara, K., Morita, H., Sao, C., Eguchi, M., Sumida, A., Murakami, K., and Matsuyoshi, S. 2008. Experience mining: building a large-scale database of personal experiences and opinions from web documents. In Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Vol. 1, pp. 314–21.
Jindal, N., and Liu, B. 2006. Identifying comparative sentences in text documents. In Proceedings of SIGIR 2006, pp. 244–51.
Jindal, N., and Liu, B. 2008. Opinion spam and analysis. In Proceedings of WSDM 2008, pp. 219–30.
Jurafsky, D., and Martin, J. 2009. Speech and Language Processing. An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2nd ed.New Jersey: Pearson Prentice Hall.
Katz, S. 1987. Estimation of probabilities from sparse data for the language model component of a speech recogniser. IEEE Transactions on Acoustics, Speech, and Signal Processing 35 (3): 400–401.
Kessler, J., and Nicolov, N. 2009. Targeting sentiment expressions through supervised ranking of linguistic configurations. In Proceedings of the 3rd International AAAI Conference on Weblogs and Social Media (ICWSM-2009).
Kim, S.-M., and Hovy, E. 2007. Crystal: analyzing predictive opinions on the web. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 1056–64.
Labov, W. 1972. Language in the Inner City: Studies in the Black English Vernacular. Oxford, UK: Blackwell.
Lasersohn, P. 2005. Context dependence, disagreement, and predicates of personal taste. Linguistics and Philosophy 28: 643–86 (Springer).
Leech, G., Deuchar, M., and Hoogenraad, R. 1982. English Grammar for Today. New York, NY: Macmillan.
Leech, G., Rayson, P., and Wilson, A. 2001. Word Frequencies in Written and Spoken English: Based on the British National Corpus. Longman.
Leech, G., and Svartvik, J. 2002. A Communicative Grammar of English, 3rd ed.Upper Saddle River, NJ: Longman.
Li, S., Huang, C-R., Zhou, G., and Lee, S. Y. M. 2010a. Employing personal/impoersonal views in supervised and semi-supervised sentiment classification. In Proceedings of ACL 2010, pp. 414–23.
Li, S., Lee, S. Y. M., Chen, Y., Huang, C-R., and Zhou, G. 2010b. Sentiment classification and polarity shifting. In Proceedings of COLING 2010, Association for Computational Linguistics, pp. 635–43.
Liu, B. 2007. Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. New York, NY: Springer.
de Marneffe, M.-C., Manning, C., and Potts, C. 2010. “Was it good? It was provocative.” Learning the meaning of scalar adjectives. In Proceedings of the ACL 2010, pp. 167–176.
Ng, V., Dasgupta, S. and Arifin, S. M. N. 2006. Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews. In Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, Association for Computational Linguistics, pp. 611–18.
Nigam, K., and Hurst, M. 2004. Towards a robust metric of opinion. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text, pp. 98–105.
Pang, B., and Lee, L. 2005. Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL-2005), pp. 115–24.
Pang, B., and Lee, L. 2008. Opinion Mining and Sentiment Analysis. The Netherlands: Now Publishers.
Pang, B., Lee, L., and Vaithyanathan, S. 2002. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of Empirical Methods of Natural Language Processing (EMNLP-2002), pp. 79–86.
Popescu, A., and Etzioni, O. 2005. Extracting product features and opinions from reviews. In Proceedings of HLTC/EMNLP 2005, Vancouver, B.C., Canada, pp. 339–46.
Quirk, R., Greenbaum, S., Leech, G., and Svartvik, J. 1985. A Comprehensive Grammar of the English Language. Upper Saddle River, NJ: Longman.
Reinratz, T. 1999. Focusing Solutions for Data Mining. New York, NY: Springer.
Riloff, E., Patwardhan, S., and Wiebe, J. 2006. Feature subsumption for opinion analysis. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 440–48.
Roget, 2006. Roget's Interactive Thesaurus. http://thesaurus.reference.com/
Scriven, M. 1981. The Logic of Evaluation. Alberta, Canada: Edgepress.
Sokolova, M., and Lapalme, G. 2008. Verbs speak loud: verb categories in learning polarity and strength of opinions. In Proceedings of the 21st Canadian Conference on Artificial Intelligence (AI-2008), pp. 320–31, New York, NY: Springer.
Sokolova, M. and Lapalme, G. 2009a. Learning opinions without using emotional words. In Proceedings of the 22nd Canadian Conference on Artificial Intelligence (AI-2009), pp. 253–56, New York, NY: Springer.
Sokolova, M. and Lapalme, G. 2009b. Opinion classification with non-affective adjectives and adverbs. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-2009), pp. 420–26.
Sokolova, M., and Szpakowicz, S. 2007. Strategies and language trends in learning success and failure of negotiations. Group Decision and Negotiation 16: 469–84 (Springer).
Somasundaran, S., and Wiebe, J. 2009. Recognizing stances in online debates. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL Suntec, Singapore, August and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Vol. 1, pp. 226–34.
Stenvall, M. 2008. On emotions and the journalistic ideals of factuality and objectivity – tools for analysis. Journal of Pragmatics 40: 1569–86 (Elsevier).
Stern, H. 1983. Fundamental Concepts of Language Teaching. Oxford, UK: Oxford University Press.
Stoyanov, V., and Cardie, C. 2008. Topic identification for fine-grained opinion analysis. In Proceedings of the COLING 2008, Vol. 1, pp. 817–24.
Strapparava, C., and Valitutti, A. 2004. Wordnet-affect: an affective extension of wordnet. In Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC-2004).
Thompson, G., and Hunston, S. 2000. Evaluation: an introduction. In Hunston, S., and Thompson, G. (eds.), Evaluation in Text. Authorial Stance and the Construction of Discourse, pp. 1–27, Oxford, UK: Oxford University Press.
Turney, P. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of ACL 2002, pp. 417–24.
Turney, P., and Littman, M. 2003. Measuring praise and criticism: inference of semantic orientation from association. ACM Transactions on Information Systems 21 (4): 315–46 (Association for Computing Machinery).
van Dijck, J. 2009. Users like you? Theorizing agency in user-generated content. Media, Culture and Society 31: 41–59.
Wilson, T., Wiebe, J., and Hwa, R. 2006. Recognizing strong and weak opinion clauses. Computational Intelligence 22 (2): 73–99 (Wiley-Blackwell).
Witten, I., and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed., Massachusetts: Morgan Kaufmann.
Yu, H., and Hatzivassiloglou, V. 2003. Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In Proceedings of EMNLP 2003, pp. 129–36.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Natural Language Engineering
  • ISSN: 1351-3249
  • EISSN: 1469-8110
  • URL: /core/journals/natural-language-engineering
Please enter your name
Please enter a valid email address
Who would you like to send this to? *

CAPTCHA *

Skip to the audio challenge
×

Metrics

Full text views

Total number of HTML views: 3
Total number of PDF views: 30 *
Loading metrics...

Abstract views

Total abstract views: 390 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 13th June 2018. This data will be updated every 24 hours.

Cancel
Confirm
×