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A Deep Learning Approach to Deal with Data Uncertainty in Sentiment Analysis

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Fuzzy Logic and Soft Computing Applications (WILF 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10147))

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Abstract

Sentiment Analysis refers to the process of computationally identifying and categorizing opinions expressed in a piece of text, in order to determine whether the writer’s attitude towards a particular topic or product is positive, negative, or even neutral. Recently, deep learning approaches emerge as powerful computational models that discover intricate semantic representations of texts automatically from data without hand-made feature engineering. These approaches have improved the state-of-the-art in many Sentiment Analysis tasks including sentiment classification of sentences or documents. In this paper we propose a semi-supervised neural network model, based on Deep Belief Networks, able to deal with data uncertainty for text sentences and adopting the Italian language as a reference language. We test this model against some datasets from literature related to movie reviews, adopting a vectorized representation of text and exploiting methods from Natural Language Processing (NLP) pre-processing.

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Notes

  1. 1.

    The Paisá corpus is made available at http://www.corpusitaliano.it through a Creative Commons license.

References

  1. Wiebe, J., Wilson, T., Bell, M.: Identifying collocations for recognizing opinions. In: Proceedings of ACL-2001 Workshop on Collocation: Computational Extraction, Analysis, and Exploitation, pp. 24–31 (2001)

    Google Scholar 

  2. Nasukawa, T., Yi, J.: Sentiment analysis: capturing favorability using natural language processing. In: Proceedings of 2nd International Conference on Knowledge Capture, K-CAP 2003, New York, NY, USA, pp. 70–77. ACM (2003)

    Google Scholar 

  3. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)

    Article  Google Scholar 

  4. Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics, ACL 1998, Stroudsburg, PA, USA, pp. 174–181. Association for Computational Linguistics (1997)

    Google Scholar 

  5. Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of 14th Conference on Computational Linguistics, COLING 1992, Stroudsburg, PA, USA, vol. 2, pp. 539–545. Association for Computational Linguistics (1992)

    Google Scholar 

  6. Wiebe, J.M.: Identifying subjective characters in narrative. In: Proceedings of 13th Conference on Computational Linguistics, COLING 1990, Stroudsburg, PA, USA, vol. 2, pp. 401–406. Association for Computational Linguistics (1990)

    Google Scholar 

  7. Wiebe, J.M.: Tracking point of view in narrative. Comput. Linguist. 20, 233–287 (1994)

    Google Scholar 

  8. Bruce, R.F., Wiebe, J.M.: Recognizing subjectivity: a case study in manual tagging. Nat. Lang. Eng. 5, 187–205 (1999)

    Article  Google Scholar 

  9. Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of 40th Annual Meeting on Association for Computational Linguistics, ACL 2002, Stroudsburg, PA, USA, pp. 417–424. Association for Computational Linguistics (2002)

    Google Scholar 

  10. Das, S., Chen, M.: Yahoo! for Amazon: extracting market sentiment from stock message boards. In: Asia Pacific Finance Association Annual Conference (APFA) (2001)

    Google Scholar 

  11. Morinaga, S., Yamanishi, K., Tateishi, K., Fukushima, T.: Mining product reputations on the web. In: Proceedings of 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, New York, NY, USA, pp. 341–349. ACM (2002)

    Google Scholar 

  12. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of ACL-2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)

    Google Scholar 

  13. Tong, R.: An operational system for detecting and tracking opinions in on-line discussions. In: Working Notes of the SIGIR Workshop on Operational Text Classification, New Orleans, Louisianna, pp. 1–6 (2001)

    Google Scholar 

  14. Wiebe, J., Wilson, T., Bruce, R., Bell, M., Martin, M.: Learning subjective language. Comput. Linguist. 30, 277–308 (2004)

    Article  Google Scholar 

  15. Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, EMNLP 2003, Stroudsburg, PA, USA, pp. 105–112. Association for Computational Linguistics (2003)

    Google Scholar 

  16. Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, EMNLP 2003, Stroudsburg, PA, USA, pp. 129–136. Association for Computational Linguistics (2003)

    Google Scholar 

  17. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, New York, NY, USA, pp. 168–177. ACM (2004)

    Google Scholar 

  18. Socher, R., Pennington, J., Huang, E.H., Ng, A.Y., Manning, C.D.: Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, Stroudsburg, PA, USA, pp. 151–161. Association for Computational Linguistics (2011)

    Google Scholar 

  19. Socher, R., Huval, B., Manning, C.D., Ng, A.Y.: Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, Stroudsburg, PA, USA, pp. 1201–1211. Association for Computational Linguistics (2012)

    Google Scholar 

  20. Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.P.: Recursive deep models for semantic compositionality over a sentiment TreeBank. In: EMNLP (2013)

    Google Scholar 

  21. Chapelle, O., Schlkopf, B., Zien, A.: Semi-Supervised Learning, 1st edn. The MIT Press, Cambridge (2010)

    Google Scholar 

  22. Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  23. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105. Curran Associates Inc. (2012)

    Google Scholar 

  24. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS 2010). Society for Artificial Intelligence and Statistics (2010)

    Google Scholar 

  25. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR, abs/1301.3781 (2013)

    Google Scholar 

  26. Stone, M.: Cross-validatory choice and assessment of statistical predictions. J. Roy. Stat. Soc. Ser. B (Methodol.) 36, 111–147 (1974)

    MathSciNet  MATH  Google Scholar 

  27. Zhou, S., Chen, Q., Wang, X., Li, X.: Hybrid deep belief networks for semi-supervised sentiment classification (2014)

    Google Scholar 

  28. Kamvar, K., Sepandar, S., Klein, K., Dan, D., Manning, M., Christopher, C.: Spectral learning. In: International Joint Conference of Artificial Intelligence. Stanford InfoLab (2003)

    Google Scholar 

  29. Collobert, R., Sinz, F., Weston, J., Bottou, L.: Large scale transductive SVMs. J. Mach. Learn. Res. 7(August), 1687–1712 (2006)

    MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Milan, Milan, Italy

    Michele Di Capua

  2. Department of Science and Technology, University of Naples “Parthenope”, Naples, Italy

    Alfredo Petrosino

Authors
  1. Michele Di Capua
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  2. Alfredo Petrosino
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Correspondence to Alfredo Petrosino .

Editor information

Editors and Affiliations

  1. University of Naples “Parthenope”, Naples, Italy

    Alfredo Petrosino

  2. University of Salerno, Fisciano, (Salerno), Italy

    Vincenzo Loia

  3. University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

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Di Capua, M., Petrosino, A. (2017). A Deep Learning Approach to Deal with Data Uncertainty in Sentiment Analysis. In: Petrosino, A., Loia, V., Pedrycz, W. (eds) Fuzzy Logic and Soft Computing Applications. WILF 2016. Lecture Notes in Computer Science(), vol 10147. Springer, Cham. https://doi.org/10.1007/978-3-319-52962-2_15

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  • DOI: https://doi.org/10.1007/978-3-319-52962-2_15

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