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Extracting Aspect Specific Sentiment Expressions Implying Negative Opinions

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Computational Linguistics and Intelligent Text Processing (CICLing 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9624))

Abstract

Subjective expression extraction is a central problem in fine-grained sentiment analysis. Most existing works focus on generic subjective expression extraction as opposed to aspect specific opinion phrase extraction. Given the ever-growing product reviews domain, extracting aspect specific opinion phrases is important as it yields the key product issues that are often mentioned via phrases (e.g., “signal fades very quickly,” “had to flash the firmware often”). In this paper, we solve the problem using a combination of generative and discriminative modeling. The generative model performs a first level processing facilitating (1) discovery of potential head aspects containing issues, (2) generation of a labeled dataset of issue phrases, and (3) feed latent semantic features to subsequent discriminative modeling. We then employ discriminative large-margin and sequence modeling with pivot features for issue sentence classification and issue phrase boundary extraction. Experimental results using real-world reviews from Amazon.com demonstrate the effectiveness of the proposed approach.

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Notes

  1. 1.

    http://www.cs.uic.edu/~liub/FBS/opinion-lexicon-English.rar.

References

  1. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of 2004 ACM SIGKDD International Conference on Knowledge Discovery Data Mining - KDD 2004. ACM Press, New York, p. 168 (2004)

    Google Scholar 

  2. Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Lang. Resour. Eval. 39, 165–210 (2005)

    Article  Google Scholar 

  3. Wang, X., McCallum, A., Wei, X.: Topical n-grams: phrase and topic discovery, with an application to information retrieval. In: 2007 Seventh IEEE International Conference on Data Mining, ICDM 2007, pp. 697–702 (2007)

    Google Scholar 

  4. Fei, G., Chen, Z., Liu, B.: Review topic discovery with phrases using the p{ó}lya urn model. In: COLING (2014)

    Google Scholar 

  5. Breck, E., Choi, Y., Cardie, C.: Identifying expressions of opinion in context. In: Proceedings of 20th International Joint Conference on Artificial Intelligence, pp. 2683–2688 (2007)

    Google Scholar 

  6. Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: Identifying sources of opinions with conditional random fields and extraction patterns. In: Proceedings of Conference on Human Language Technology and Empirical Methods in Natural Language Processing – HLT 2005. Association for Computational Linguistics, Morristown, NJ, USA, pp. 355–362 (2005)

    Google Scholar 

  7. Choi, Y., Breck, E., Cardie, C.: Joint extraction of entities and relations for opinion recognition. In: Proceedings of 2006 Conference on Empirical Methods in Natural Language Processing, pp. 431–439 (2006)

    Google Scholar 

  8. Li, H., Mukherjee, A., Liu, B., Si, J.: Extracting verb expressions implying negative opinions. In: Proceedings of Twenty-ninth AAAI Conference on Artificial Intelleligence (2015)

    Google Scholar 

  9. Berend, G.: Opinion expression mining by exploiting keyphrase extraction. In: International Joint Conference on Natural Language Processing, pp. 1162–1170 (2011)

    Google Scholar 

  10. Johansson, R., Moschitti, A.: Reranking models in fine-grained opinion analysis. In: Proceedings of 23rd International Conference on Computational Linguistics, pp. 519–527 (2010)

    Google Scholar 

  11. Yang, B., Cardie, C.: Extracting opinion expressions with semi-Markov conditional random fields. In: Proceedings of 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1335–1345 (2012)

    Google Scholar 

  12. Klinger, R., Cimiano, P.: Bidirectional inter-dependencies of subjective expressions and targets and their value for a joint model. In: Association for Computational Linguistics (Short Paper) (2013)

    Google Scholar 

  13. Kim, S.-M., Hovy, E.: Extracting opinions, opinion holders, and topics expressed in online news media text. In: Proceedings of the Workshop on Sentiment and Subjectivity in Text, pp. 1–8 (2006)

    Google Scholar 

  14. Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: Proceedings of 2009 Conference on Empirical Methods in Natural Language Processing, pp. 1533–1541 (2009)

    Google Scholar 

  15. Jakob, N., Gurevych, I.: Extracting opinion targets in a single- and cross-domain setting with conditional random fields. In: Proceedings of 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1035–1045 (2010)

    Google Scholar 

  16. Kobayashi, N., Inui, K., Matsumoto, Y.: Extracting aspect-evaluation and aspect-of relations in opinion mining. In: Proceedings of 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (2007)

    Google Scholar 

  17. Johansson, R., Moschitti, A.: Extracting opinion expressions and their polarities: exploration of pipelines and joint models. In: Association for Computational Linguistics (Short Paper), pp. 101–106 (2011)

    Google Scholar 

  18. Yang, B., Cardie, C.: Joint modeling of opinion expression extraction and attribute classification. Trans. Assoc. Comput. Linguist. 2, 505–516 (2014)

    Google Scholar 

  19. Sauper, C., Haghighi, A., Barzilay, R.: Content models with attitude. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 350–358 (2011)

    Google Scholar 

  20. Yang, B., Cardie, C.: Joint inference for fine-grained opinion extraction. In: Association for Computational Linguistics, pp. 1640–1649 (2013)

    Google Scholar 

  21. Barzilay, R., McKeown, K.R.: Extracting paraphrases from a parallel corpus. In: Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics, pp. 50–57 (2001)

    Google Scholar 

  22. Apidianaki, M., Verzeni, E., McCarthy, D.: Semantic clustering of pivot paraphrases. In: Conference on Language Resources and Evaluation, pp. 4270–4275 (2014)

    Google Scholar 

  23. Keshtkar, F., Inkpen, D.: A corpus-based method for extracting paraphrases of emotion terms. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 35–44 (2010)

    Google Scholar 

  24. Hasan, K.S., Ng, V.: Automatic keyphrase extraction: a survey of the state of the art. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 1262–1273 (2014)

    Google Scholar 

  25. Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International Conference on World Wide Web, pp. 111–120 (2008)

    Google Scholar 

  26. Brody, S., Elhadad, N.: An unsupervised aspect-sentiment model for online reviews. In: Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 804–812 (2010)

    Google Scholar 

  27. Mukherjee, A., Liu, B.: Aspect extraction through semi-supervised modeling. In: Association for Computational Linguistics (2012)

    Google Scholar 

  28. Zhao, W.X., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 56–65 (2010)

    Google Scholar 

  29. Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. 101, 5228–5235 (2004)

    Article  Google Scholar 

  30. Chang, J., Gerrish, S., Wang, C., Boyd-graber, J.L., Blei, D.M.: Reading tea leaves: how humans interpret topic models. In: Advances in Neural Information Processing Systems, pp. 288–296 (2009)

    Google Scholar 

  31. Pontiki, M., Galanis, D., Papageogiou, H., Manandhar, S., Androutsopoulos, I.: Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Color (2015)

    Google Scholar 

  32. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing – HLT 2005, Morristown, NJ, USA. Association for Computational Linguistics, pp. 347–354 (2005)

    Google Scholar 

  33. Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 129–136 (2003)

    Google Scholar 

  34. Joachims, T.: Making large-scale support vector machine learning practical. In: Advance in Kernel Methods (1999)

    Google Scholar 

  35. Ramshaw, L.A., Marcus, M.P.: Text chunking using transformation-based learning. In: Armstrong, S., Church, K., Isabelle, P., Manzi, S., Tzoukermann, E., Yarowsky, D. (eds.) Natural Language Processing Using Very Large Corpora. Text, Speech and Language Technology, vol. 11. Springer, Dordrecht (1995). https://doi.org/10.1007/978-94-017-2390-9_10

    Google Scholar 

  36. Li, Y., Jiang, J., Chieu, H.L., Chai, K.M.A.: Extracting relation descriptors with conditional random fields. In: International Joint Conference on Natural Language Processing, pp. 392–400 (2011)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, University of Houston, Houston, USA

    Arjun Mukherjee

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  1. Arjun Mukherjee
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Correspondence to Arjun Mukherjee .

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  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Mukherjee, A. (2018). Extracting Aspect Specific Sentiment Expressions Implying Negative Opinions. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9624. Springer, Cham. https://doi.org/10.1007/978-3-319-75487-1_15

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  • DOI: https://doi.org/10.1007/978-3-319-75487-1_15

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