Advertisement

An Opinion Mining Model for Generic Domains

  • Franco Tuveri
  • Manuela Angioni
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 515)

Abstract

Online users are talking across social media sites, on public forums and within customer feedback channels about products, services and their experiences, as well as their likes and dislikes. The continuous monitoring of reviews is ever more important in order to identify leading topics and content categories and to understand how those topics and categories are relevant to customers according to their habits. In this context, the chapter proposes an Opinion Mining model to analyze and summarize reviews related to generic categories of products and services. The process, based on a linguistic approach to the analysis of the opinions expressed, includes the extraction of features terms from the reviews in generic domains. It is also capable to determine the positive or negative valence of the identified features exploiting FreeWordNet, a WordNet-based linguistic resource of adjectives and adverbs involved in the whole process.

Keywords

Opinion mining Sentiment analysis Text categorization Feature extraction Opinion summarization 

References

  1. 1.
    Gartner (ed.): Gartner’s 2012 Hype Cycle for Emerging Technologies Identifies “Tipping Point” Technologies That Will Unlock Long-Awaited Technology Scenarios. http://www.gartner.com/it/page.jsp?id=2124315 (2012)
  2. 2.
    Hexagon, C. (ed.): Listen, Understand, Act. How a listening platform provides actionable insight. www.crimsonhexagon.com/PDFs/Crimson_Hexagon_Listen_Understand_Feb_2009.pdf (2009)
  3. 3.
    Grimes, S., DeepMR: Market Research Mines Social Sentiment. http://www.greenbookblog.org/2012/05/01/deepmr-market-research-mines-social-sentiment/ (2012)
  4. 4.
    Jordan S.J.: 5 Dangers of DIY Research. http://www.researchplan.com/blog/?p=51 (2012)
  5. 5.
  6. 6.
    Ding, X., Liu, B., Yu, P.S.: A Holistic Lexicon-Based Approach to Opinion Mining. WSDM ’08 Proceedings of the international conference on Web search and web data mining, pp. 231–240. ACM, New York (2008)Google Scholar
  7. 7.
    Akkaya, C., Wiebe, J., Mihalcea, R.: Subjectivity word sense disambiguation. In: Conference on Empirical Methods in Natural Language Processing, Singapore, pp. 190–199. The Association for Computational Linguistics (2008)Google Scholar
  8. 8.
    Rentoumi, V., Giannakopoulos, G.: Sentiment analysis of figurative language using a word sense disambiguation approach. In: International Conference on Recent Advances in Natural Language Processing (RANLP 2009), Borovets, Bulgaria, pp. 370–375. The Association for Computational Linguistics (2008)Google Scholar
  9. 9.
    Lee, D., Jeong, O.R., Lee, S.: Opinion mining of customer feedback data on the web. In: ICUIMC ’08 Proceedings of the 2nd International Conference on Ubiquitous Information Management Communication, pp. 230–235 (2008)Google Scholar
  10. 10.
    Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H., Jin, C.: Red Opal: product-feature scoring from reviews. In: ACM Conference on Electronic Commerce, 2007, pp. 182–191 (2007)Google Scholar
  11. 11.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM Press (2004)Google Scholar
  12. 12.
    Zhai, Z., Liu, B., Xu, H., Jia, P.: Grouping Product Features Using Semi-Supervised Learning with Soft-Constraints. In Proceedings of the 23rd International Conference on Computational Linguistics (COLING-2010), Beijing, China, pp. 1272–1280 (2010)Google Scholar
  13. 13.
    Popescu, A., M., and Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of the 2005 Conference on Empirical Methods in Natural Language Processing, pp. 339–346 (2005)Google Scholar
  14. 14.
    Kim, H.D., Ganesan, K., Sondhi, P., Zhai, C.X.: Comprehensive Review of Opinion Summarization. UIUC Technical Report, USA (2011)Google Scholar
  15. 15.
    Tuveri, F., Angioni, M.: A Linguistic Approach to Feature Extraction Based on a Lexical Database of the Properties of Adjectives and Adverbs, Global WordNet Conference (GWC2012), pp. 365–370. Matsue, Japan (2012)Google Scholar
  16. 16.
    Atserias, J., Casas, B., Comelles, E., González, M., Padró, L., Padró, M.: FreeLing 1.3: Syntactic and semantic services in an open-source NLP library. In: Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC 2006), ELRA. Genoa, Italy, pp. 48–55. http://nlp.lsi.upc.edu/freeling (2006)
  17. 17.
    Vossen, P. (ed.): EuroWordNet: A Multilingual Database with Lexical Semantic Net-works. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
  18. 18.
    Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Proceedings of the International Conference on New Methods in Language Processing, pp. 44–49 (1994)Google Scholar
  19. 19.
    Leacock, C. and Chodorow, M.: Combining local context and WordNet similarity for word sense identification. In: Fellbaum, pp. 265–283 (1998)Google Scholar
  20. 20.
    Michelizzi, J.; Semantic relatedness applied to all words sense disambiguation. Thesis submitted to the University of Minnesota, Duluth. Major: Computer science (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.CRS4, Center of Advanced StudiesResearch and Development in SardiniaSardiniaItaly

Personalised recommendations