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Inferring Aspect-Specific Opinion Structure in Product Reviews Using Co-training

  • Dave CarterEmail author
  • Diana Inkpen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9042)

Abstract

Opinions expressed about a particular subject are often nuanced: a person may have both negative and positive opinions about different aspects of the subject of interest, and these aspect-specific opinions can be independent of the overall opinion. Being able to identify, collect, and count these nuanced opinions in a large set of data offers more insight into the strengths and weaknesses of competing products and services than does aggregating overall ratings. We contribute a new confidence-based co-training algorithm that can identify product aspects and sentiments expressed about such aspects. Our algorithm offers better precision than existing methods, and handles previously unseen language well. We show competitive results on a set of opinionated sentences about laptops and restaurants from a SemEval-2014 Task 4 challenge.

Keywords

Natural Language Processing Opinion Mining Sentiment Analysis Unlabelled Data Parse Tree 
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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References

  1. 1.
    Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A.A., Lally, A., Murdock, J.W., Nyberg, E., Prager, J., et al.: Building watson: An overview of the deepqa project. AI Magazine 31, 59–79 (2010)Google Scholar
  2. 2.
    Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Mining Text Data, pp. 415–463. Springer (2012)Google Scholar
  3. 3.
    Liu, B.: Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5, 1–167 (2012)CrossRefGoogle Scholar
  4. 4.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2, 1–135 (2008)CrossRefGoogle Scholar
  5. 5.
    Ghose, A., Ipeirotis, P.G.: Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering 23, 1498–1512 (2011)CrossRefGoogle Scholar
  6. 6.
    Archak, N., Ghose, A., Ipeirotis, P.G.: Show me the money!: Deriving the pricing power of product features by mining consumer reviews. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2007, pp. 56–65. ACM, New York (2007)Google Scholar
  7. 7.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 168–177. ACM, New York (2004)CrossRefGoogle Scholar
  8. 8.
    Popescu, A.M., Etzioni, O.: Extracting product features and opinions from reviews. In: Natural Language Processing and Text Mining, pp. 9–28. Springer (2007)Google Scholar
  9. 9.
    Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, WSDM 2008, pp. 231–240. ACM, New York (2008)Google Scholar
  10. 10.
    Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In: ACL, vol. 7, pp. 440–447 (2007)Google Scholar
  11. 11.
    Nasukawa, T., Yi, J.: Sentiment analysis: Capturing favorability using natural language processing. In: Proceedings of the 2nd International Conference on Knowledge Capture, K-CAP 2003, pp. 70–77. ACM, New York (2003)Google Scholar
  12. 12.
    Titov, I., McDonald, R.: A joint model of text and aspect ratings for sentiment summarization. In: Proc. ACL 2008: HLT, pp. 308–316 (2008)Google Scholar
  13. 13.
    Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International Conference on World Wide Web, WWW 2008, pp. 111–120. ACM, New York (2008)Google Scholar
  14. 14.
    Jo, Y., Oh, A.H.: Aspect and sentiment unification model for online review analysis. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 815–824. ACM, New York (2011)Google Scholar
  15. 15.
    Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.: Topic sentiment mixture: modeling facets and opinions in weblogs. In: Proceedings of the 16th International Conference on World Wide Web, pp. 171–180. ACM (2007)Google Scholar
  16. 16.
    Glance, N., Hurst, M., Nigam, K., Siegler, M., Stockton, R., Tomokiyo, T.: Deriving marketing intelligence from online discussion. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, KDD 2005, pp. 419–428. ACM, New York (2005)Google Scholar
  17. 17.
    Brody, S., Elhadad, N.: An unsupervised aspect-sentiment model for online reviews. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, HLT 2010, pp. 804–812. Association for Computational Linguistics, Stroudsburg (2010)Google Scholar
  18. 18.
    Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web, WWW 2003, pp. 519–528. ACM, New York (2003)Google Scholar
  19. 19.
    Gamon, M., Aue, A., Corston-oliver, S., Ringger, E.: Pulse: Mining customer opinions from free text. In: Proc. of the 6th International Symposium on Intelligent Data Analysis, pp. 121–132 (2005)Google Scholar
  20. 20.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, pp. 92–100. ACM, New York (1998)Google Scholar
  21. 21.
    Collins, M., Singer, Y.: Unsupervised models for named entity classification. In: Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 100–110 (1999)Google Scholar
  22. 22.
    Goldman, S., Zhou, Y.: Enhancing supervised learning with unlabeled data. In: Proceedings of the 17th International Conference on Machine Learning, pp. 327–334. Morgan Kaufmann (2000)Google Scholar
  23. 23.
    Dasgupta, S., Littman, M.L., McAllester, D.: Pac generalization bounds for co-training. Advances in Neural Information Processing Systems 1, 375–382 (2002)Google Scholar
  24. 24.
    Abney, S.: Bootstrapping. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL 2002, pp. 360–367. Association for Computational Linguistics, Stroudsburg (2002)Google Scholar
  25. 25.
    Wang, W., Zhou, Z.H.: Co-training with insufficient views. In: Asian Conference on Machine Learning, pp. 467–482 (2013)Google Scholar
  26. 26.
    Balcan, M.F., Blum, A., Yang, K.: Co-training and expansion: Towards bridging theory and practice. In: Advances in Neural Information Processing Systems, pp. 89–96 (2004)Google Scholar
  27. 27.
    Du, J., Ling, C.X., Zhou, Z.H.: When does cotraining work in real data? IEEE Trans. on Knowl. and Data Eng. 23, 788–799 (2011)CrossRefGoogle Scholar
  28. 28.
    Nigam, K., Ghani, R.: Analyzing the effectiveness and applicability of co-training. In: Proceedings of the Ninth International Conference on Information and Knowledge Management, CIKM 2000, pp. 86–93. ACM, New York (2000)Google Scholar
  29. 29.
    Huang, J., Sayyad-Shirabad, J., Matwin, S., Su, J.: Improving multi-view semi-supervised learning with agreement-based sampling. Intell. Data Anal., 745–761 (2012)Google Scholar
  30. 30.
    Pierce, D., Cardie, C.: Limitations of co-training for natural language learning from large datasets. In: Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing, pp. 1–9 (2001)Google Scholar
  31. 31.
    Wang, W., Zhou, Z.-H.: Analyzing co-training style algorithms. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 454–465. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  32. 32.
    Wan, X.: Bilingual co-training for sentiment classification of chinese product reviews. Computational Linguistics 37, 587–616 (2011)CrossRefGoogle Scholar
  33. 33.
    Liu, S., Li, F., Li, F., Cheng, X., Shen, H.: Adaptive co-training svm for sentiment classification on tweets. In: Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, CIKM 2013, pp. 2079–2088. ACM, New York (2013)Google Scholar
  34. 34.
    Liu, S., Zhu, W., Xu, N., Li, F., Cheng, X.Q., Liu, Y., Wang, Y.: Co-training and visualizing sentiment evolvement for tweet events. In: Proceedings of the 22nd International Conference on World Wide Web Companion, WWW 2013 Companion, pp. 105–106. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva (2013)Google Scholar
  35. 35.
    Biyani, P., Caragea, C., Mitra, P., Zhou, C., Yen, J., Greer, G.E., Portier, K.: Co-training over domain-independent and domain-dependent features for sentiment analysis of an online cancer support community. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, pp. 413–417. ACM, New York (2013)Google Scholar
  36. 36.
    Kiritchenko, S., Matwin, S.: Email classification with co-training. In: Proceedings of the 2001 Conference of the Centre for Advanced Studies on Collaborative Research, CASCON 2001, p. 8. IBM Press (2001)Google Scholar
  37. 37.
    Sarkar, A.: Applying co-training methods to statistical parsing. In: Proceedings of the Second Meeting of the North American Chapter of the Association for Computational Linguistics on Language Technologies, NAACL 2001, pp. 1–8. Association for Computational Linguistics, Stroudsburg (2001)Google Scholar
  38. 38.
    Mihalcea, R.: Co-training and self-training for word sense disambiguation. In: Proceedings of the Conference on Computational Natural Language Learning, CoNLL 2004 (2004)Google Scholar
  39. 39.
    Ng, V., Cardie, C.: Bootstrapping coreference classifiers with multiple machine learning algorithms. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, EMNLP 2003, pp. 113–120. Association for Computational Linguistics, Stroudsburg (2003)Google Scholar
  40. 40.
    Clark, S., Curran, J.R., Osborne, M.: Bootstrapping pos taggers using unlabelled data. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, CONLL 2003, vol. 4, pp. 49–55. Association for Computational Linguistics, Stroudsburg (2003)Google Scholar
  41. 41.
    Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: Semeval-2014 task 4: Aspect based sentiment analysis. In: Proceedings of the International Workshop on Semantic Evaluation (SemEval) (2014)Google Scholar
  42. 42.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), http://www.csie.ntu.edu.tw/~cjlin/libsvm
  43. 43.
    Ganu, G., Elhadad, N., Marian, A.: Beyond the stars: Improving rating predictions using review text content. In: Proceedings of the 12th International Workshop on the Web and Databases, WebDB 2009 (2009)Google Scholar
  44. 44.
    Nigam, K., Hurst, M.: Towards a robust metric of opinion. In: AAAI Spring Symposium on Exploring Attitude and Affect in Text, pp. 598–603 (2004)Google Scholar
  45. 45.
    Wilson, T., Wiebe, J., Hwa, R.: Just how mad are you? finding strong and weak opinion clauses. In: Proceedings of AAAI, pp. 761–769 (2004)Google Scholar
  46. 46.
    Turney, P.D., Littman, M.L.: Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems 21, 315–346 (2003)CrossRefGoogle Scholar
  47. 47.
    Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: Proceedings of the 33rd Annual Meeting on Association for Computational Linguistics, ACL 1995, pp. 189–196. Association for Computational Linguistics, Stroudsburg (1995)Google Scholar

Copyright information

© Her Majesty the Queen in Right of Canada 2015

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  2. 2.National Research CouncilOttawaCanada

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