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An Ontology-Based Approach to Sentiment Classification of Mixed Opinions in Online Restaurant Reviews

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Social Informatics (SocInfo 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8238))

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Abstract

Consumers review other consumer’s opinion and experience of the quality of various products before making purchase. Automatic sentiment analysis of WOM in the form of user product reviews, blog posts and comments in online forum can support strategies in areas such as search engines, recommender systems, and market research and benefit to both consumers and sellers. The ontology-based approach designed in this work aims to investigate how to detect and classify mixed positive and negative opinions by interpreting with an ontology containing opinion information on terms. Our research question is whether disinterested subjectivity scores of sentiment ontology are pertinent to sentiment orientations not affected by reviewer’s linguistic bias. The experimental results adopting opinion lexical resource achieve better and more stable performance in F-measure.

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References

  1. Chevalier, J.A., Mayzlin, D.: The Effect of Word of Mouth on Sales: Online Book Reviews. No. w10148. National Bureau of Economic Research (2003)

    Google Scholar 

  2. Chen, Y., Xie, J.: Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix. Manage. Sci. 54(3), 477–491 (2008)

    Article  Google Scholar 

  3. Ohana, B., Tierney, B.: Sentiment Classification of Reviews Using SentiWordNet. In: 9th IT & T Conference, p. 13 (2009)

    Google Scholar 

  4. Ye, Q., Shi, W., Li, Y.: Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach. In: 39th Annual Hawaii International Conference on System Sciences, HICSS 2006, vol. 3, p. 53b. IEEE (2006)

    Google Scholar 

  5. Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment Strength Detection for the Social Web. Journal of the American Society for Information Science and Technology 63(1), 163–173 (2012)

    Article  Google Scholar 

  6. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs Up?: Sentiment Classification Using Machine Learning Techniques. In: ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)

    Google Scholar 

  7. Cui, H., Mittal, V., Datar, M.: Comparative Experiments on Sentiment Classification for Online Product Reviews. In: AAAI, vol. 6, pp. 1265–1270 (2006)

    Google Scholar 

  8. Ye, Q., Zhang, Z., Law, R.: Sentiment Classification of Online Reviews to Travel Destinations by Supervised Machine Learning Approaches. Expert Systems with Applications 36(3), 6527–6535 (2009)

    Article  Google Scholar 

  9. Li, S., Wang, Z., Zhou, G., Lee, S.Y.M.: Semi-Supervised Learning for Imbalanced Sentiment Classification. In: 22nd International Joint Conference on Artificial Intelligence, vol. 3, pp. 1826–1831. AAAI Press (2011)

    Google Scholar 

  10. Hu, M., Liu, B.: Mining and Summarizing Customer Reviews. In: 10th ACM SIGKDD, pp. 168–177. ACM (2004)

    Google Scholar 

  11. Titov, I., McDonald, R.: Modeling Online Reviews with Multi-Grain Topic Models. In: 17th International Conference on World Wide Web, pp. 111–120. ACM (2008)

    Google Scholar 

  12. Li, F., Liu, N., Jin, H., Zhao, K., Yang, Q., Zhu, X.: Incorporating Reviewer and Product Information for Review Rating Prediction. In: 22nd International Joint Conference on Artificial Intelligence, vol. 3, pp. 1820–1825. AAAI Press (2011)

    Google Scholar 

  13. de Albornoz, J.C., Plaza, L., Gervás, P., Díaz, A.: A Joint Model of Feature Mining and Sentiment Analysis for Product Review Rating. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 55–66. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  14. Pan, S.J., Ni, X., Sun, J.T., Yang, Q., Chen, Z.: Cross-Domain Sentiment Classification via Spectral Feature Alignment. In: 19th International Conference on World Wide Web, pp. 751–760. ACM (2010)

    Google Scholar 

  15. Dave, K., Lawrence, S., Pennock, D.M.: Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews. In: 12th International Conference on World Wide Web, pp. 519–528. ACM (2003)

    Google Scholar 

  16. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In: Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354. Association for Computational Linguistics (2005)

    Google Scholar 

  17. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment Strength Detection in Short Informal Text. Journal of the American Society for Information Science and Technology 61(12), 2544–2558 (2010)

    Article  Google Scholar 

  18. Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. In: LREC, vol. 10, pp. 2200–2204 (2010)

    Google Scholar 

  19. Ghose, A., Ipeirotis, P.G.: Designing Novel Review Ranking Systems: Predicting the Usefulness and Impact of Reviews. In: 9th International Conference on Electronic Commerce, pp. 303–310. ACM (2007)

    Google Scholar 

  20. Hu, X., Downie, J.S., West, K., Ehmann, A.: Mining Music Reviews: Promising Preliminary Results. In: 6th International Symposium on Music Information Retrieval (2005)

    Google Scholar 

  21. Jin, W., Ho, H.H., Srihari, R.K.: OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction. In: 15th ACM SIGKDD, pp. 1195–1204. ACM (2009)

    Google Scholar 

  22. Turney, P.D.: Thumbs Up or Thumbs Down?: Semantic Orientation Applied to Unsupervised Classification of Reviews. In: 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424. Association for Computational Linguistics (2002)

    Google Scholar 

  23. Hatzivassiloglou, V., McKeown, K.R.: Predicting the Semantic Orientation of Adjectives. In: 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics, pp. 174–181. Association for Computational Linguistics (1997)

    Google Scholar 

  24. Miller, G.A.: WordNet: ALexical Database for English. Communications of the ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  25. Zhuang, L., Jing, F., Zhu, X.Y.: Movie Review Mining and Summarization. In: 15th ACM International Conference on Information and Knowledge Management, pp. 43–50. ACM (2006)

    Google Scholar 

  26. Ding, X., Liu, B., Yu, P.S.: A Holistic Lexicon-Based Approach to Opinion Mining. In: International Conference on Web Search and Web Data Mining, pp. 231–240. ACM (2008)

    Google Scholar 

  27. Andreevskaia, A., Bergler, S.: Mining WordNet for a Fuzzy Sentiment: Sentiment Tag Extraction from WordNet Glosses. In: EACL, pp. 209–216 (2006)

    Google Scholar 

  28. Esuli, A., Sebastiani, F.: SentiWordNet: A Publicly Available Lexical Resource for Opinion mining. In: LREC, vol. 6, pp. 417–422 (2006)

    Google Scholar 

  29. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics 37(2), 267–307 (2011)

    Article  Google Scholar 

  30. Yang, Y., Pedersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: ICML, vol. 97, pp. 412–420 (1997)

    Google Scholar 

  31. Saggionα, H., Funk, A.: Interpreting SentiWordNet for Opinion Classification. In: 7th Conference on International Language Resources and Evaluation, LREC 2010 (2010)

    Google Scholar 

  32. Hung, C., Lin, H.: Using Objective Words in SentiWordNet to Improve Sentiment Classification for Word of Mouth. IEEE Intelligent Systems 28(2), 47–54 (2013)

    Article  Google Scholar 

  33. Abulaish, M., Jahiruddin, Doja, M.N., Ahmad, T.: Feature and Opinion Mining for Customer Review Summarization. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds.) PReMI 2009. LNCS, vol. 5909, pp. 219–224. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  34. Denecke, K.: Using SentiWordNet for Multilingual Sentiment Analysis. In: IEEE 24th International Conference on Data Engineering Workshop, ICDEW 2008, pp. 507–512. IEEE (2008)

    Google Scholar 

  35. Hamouda, A., Rohaim, M.: Reviews Classification Using SentiWordNetLexicon. In: World Congress on Computer Science and Information Technology (2011)

    Google Scholar 

  36. Peñalver-Martínez, I., Valencia-García, R., García-Sánchez, F.: Ontology-Guided Approach to Feature-Based Opinion Mining. In: Muñoz, R., Montoyo, A., Métais, E. (eds.) NLDB 2011. LNCS, vol. 6716, pp. 193–200. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  37. Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.: Topic Sentiment Mixture: Modeling facets and Opinions in Weblogs. In: 16th International Conference on World Wide Web, pp. 171–180. ACM (2007)

    Google Scholar 

  38. Wang, C., Blei, D.M.: Collaborative Topic Modeling for Recommending Scientific Articles. In: 17th ACM SIGKDD, pp. 448–456. ACM (2011)

    Google Scholar 

  39. Go, A., Bhayani, R., Huang, L.: Twitter Sentiment Classification Using Distant Supervision. CS224N Project Report, pp. 1-12 (2009)

    Google Scholar 

  40. Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-Dependent Twitter Sentiment Classification. In: ACL, pp. 151–160 (2011)

    Google Scholar 

  41. Kouloumpis, E., Wilson, T., Moore, J.: Twitter Sentiment Analysis: The Good the Bad and the OMG! In: ICWSM (2011)

    Google Scholar 

  42. Barbosa, L., Feng, J.: Robust Sentiment Detection on Twitter from Biased and Noisy Data. In: 23rd International Conference on Computational Linguistics: Posters, pp. 36–44. Association for Computational Linguistics (2010)

    Google Scholar 

  43. Wikipedia.org, http://en.wikipedia.org/wiki/Yelp.com

  44. Akbani, R., Kwek, S., Japkowicz, N.: Applying Support Vector Machines to Imbalanced Datasets. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 39–50. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  45. Wu, G., Chang, E.Y.: Class-Boundary Alignment for Imbalanced Dataset Learning. In: ICML 2003 Workshop on Learning from Imbalanced Data Sets II, pp. 49–56 (2003)

    Google Scholar 

  46. Chang, C.C., Lin, C.J.: LIBSVM: ALibrary for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2(3), 27 (2011)

    Google Scholar 

  47. EL-Manzalawy, Y., Honavar, V.: WLSVM: Integrating LibSVM into Weka Environment (2005), http://www.cs.iastate.edu/~yasser/wlsvm

  48. Foody, G.M.: Status of Land Cover Classification Accuracy Assessment. Remote Sensing of Environment 80(1), 185–201 (2002)

    Article  Google Scholar 

  49. Yang, Y., Pedersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: ICML, vol. 97, pp. 412–420 (1997)

    Google Scholar 

  50. van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworth (1979)

    Google Scholar 

  51. Musicant, D.R., Kumar, V., Ozgur, A.: Optimizing F-Measure with Support Vector Machines. In: FLAIRS Conference, pp. 356–360 (2003)

    Google Scholar 

  52. Douglas, K.M., Sutton, R.M., Wilkin, K.: Could You Mind YourLanguage? An Investigation of Communicators’ Ability to Inhibit Linguistic Bias. Journal of Language and Social Psychology 27(2), 123–139 (2008)

    Article  Google Scholar 

  53. Ptaszynski, M., Dybala, P., Shi, W., Rzepka, R.: Contextual Affect Analysis: A Aystem for Verification of Emotion Appropriateness Supported with Contextual Valence Shifters. Int. J. Biometrics 2(2), 134–154 (2010)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Library and Information Science, Yonsei University, Seoul, Korea

    Hea-Jin Kim & Min Song

Authors
  1. Hea-Jin Kim
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  2. Min Song
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Editor information

Editors and Affiliations

  1. Department of Social Informatics, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, 606-8501, Sakyo-ku, Kyoto, Japan

    Adam Jatowt

  2. School of Information Systems, Singapore Management University, 80 Stamford Road, 178902, Singapore

    Ee-Peng Lim  & Bing Tian Dai  & 

  3. Department of Information and Library Science, School of Informatics and Computing, Indiana University, 1320 E. 10th St., LI 011, 47405, Bloomington, IN, USA

    Ying Ding

  4. Department of Integrated Psychological Sciences, Kwansei Gakuin University, 1-1-155 Uegahara, 662-8501, Nishinomiya, Hyogo, Japan

    Asako Miura

  5. Faculty of Library, Information and Media Science, University of Tsukuba, 1-2 Kasuga, 305-8550, Tsukuba City, Ibaraki, Japan

    Taro Tezuka

  6. Department of Computer Science, University of Caen Basse-Normandie, Campus Côte de Nacre, F-14032, Caen Cedex, France

    Gaël Dias

  7. Department of Social Informatics, Graduate School of Informatics,, Kyoto University, 606-8501, Kyoto, Japan

    Katsumi Tanaka

  8. Department of Communication, University of California, 93106, Santa Barbara, CA, USA

    Andrew Flanagin

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Kim, HJ., Song, M. (2013). An Ontology-Based Approach to Sentiment Classification of Mixed Opinions in Online Restaurant Reviews. In: Jatowt, A., et al. Social Informatics. SocInfo 2013. Lecture Notes in Computer Science, vol 8238. Springer, Cham. https://doi.org/10.1007/978-3-319-03260-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-03260-3_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03259-7

  • Online ISBN: 978-3-319-03260-3

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