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A Linguistic Rule-Based Approach for Aspect-Level Sentiment Analysis of Movie Reviews

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 553))

Abstract

Aspect-level sentiment analysis refers to sentiment polarity detection from unstructured text at a fine-grained feature or aspect level. This paper presents our experimental work on aspect-level sentiment analysis of movie reviews. Movie reviews generally contain user opinion about different aspects such as acting, direction, choreography, cinematography, etc. We have devised a linguistic rule-based approach which identifies the aspects from movie reviews, locates opinion about that aspect and computes the sentiment polarity of that opinion using linguistic approaches. The system generates an aspect-level opinion summary. The experimental design is evaluated on datasets of two movies. The results achieved good accuracy and shows promise for deployment in an integrated opinion profiling system.

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Notes

  1. 1.

    http://nlp.stanford.edu/software/tagger.shtml.

References

  1. Pang, B., Lee, L., & Vaithyanathan, S. (2002, July). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10 (pp. 79–86). Association for Computational Linguistics.

    Google Scholar 

  2. Pang, B., & Lee, L. (2004, July). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd annual meeting on Association for Computational Linguistics (p. 271). Association for Computational Linguistics.

    Google Scholar 

  3. Pang, B., & Lee, L. (2005, June). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (pp. 115–124). Association for Computational Linguistics.

    Google Scholar 

  4. Gamon, M. (2004, August). Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In Proceedings of the 20th international conference on Computational Linguistics (p. 841). Association for Computational Linguistics.

    Google Scholar 

  5. Durant, K. T., & Smith, M. D. (2006, August). Mining sentiment classification from political web logs. In Proceedings of Workshop on Web Mining and Web Usage Analysis of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (WebKDD-2006), Philadelphia, PA.

    Google Scholar 

  6. Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Proceedings of the 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’04. doi:10.1145/1014052.1014073

  7. Carenini, G., Ng, R. T., & Zwart, E. (2005). Extracting knowledge from evaluative text. Proceedings of the 3rd International Conference on Knowledge Capture - K-CAP ’05. doi:10.1145/1088622.1088626

  8. Popescu, A. M., & Etzioni, O. (2007). Extracting product features and opinions from reviews. In Natural language processing and text mining (pp. 9–28). Springer London.

    Google Scholar 

  9. Blair-Goldensohn, S., Hannan, K., McDonald, R., Neylon, T., Reis, G. A., & Reynar, J. (2008, April). Building a sentiment summarizer for local service reviews. In WWW Workshop on NLP in the Information Explosion Era (Vol. 14).

    Google Scholar 

  10. Yi, J., Nasukawa, T., Bunescu, R., & Niblack, W. (2003, November). Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Data Mining, 2003. ICDM 2003. Third IEEE International Conference on (pp. 427–434). IEEE.

    Google Scholar 

  11. Eirinaki, M., Pisal, S., & Singh, J. (2012). Feature-based opinion mining and ranking. Journal of Computer and System Sciences, 78(4), 1175–1184. doi:10.1016/j.jcss.2011.10.007

  12. Lu, Y., Zhai, C., & Sundaresan, N. (2009, April). Rated aspect summarization of short comments. In Proceedings of the 18th international conference on World Wide Web (pp. 131–140). ACM.

    Google Scholar 

  13. Titov, I., & McDonald, R. (2008, April). Modeling online reviews with multi-grain topic models. In Proceedings of the 17th international conference on World Wide Web (pp. 111–120). ACM.

    Google Scholar 

  14. Singh, V. K., Piryani, R., Uddin, A., & Waila, P. (2013, March). Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification. In Automation, Computing, Communication, Control and Compressed Sensing (iMac4 s), 2013 International Multi-Conference on (pp. 712–717). IEEE.

    Google Scholar 

  15. Singh, V. K., Piryani, R., Walia, P., & Devaraj, M. (2014). Computing Sentiment Polarity of Texts at Document and Aspect Levels. ECTI Transaction On computer and Information Technology, 8(1).

    Google Scholar 

  16. Thet, T. T., Na, J. C., & Khoo, C. S. (2010). Aspect-based sentiment analysis of movie reviews on discussion boards. Journal of Information Science, 0165551510388123.

    Google Scholar 

  17. Rolling, L. (1981). Indexing consistency, quality and efficiency. Information Processing & Management, 17(2), 69–76.

    Google Scholar 

  18. Byrt, T. (1996). How Good Is That Agreement? Epidemiology, 7(5), 561.

    Google Scholar 

  19. Singh, V. K., Mukherjee, M., & Mehta, G. K. (2011, December). Combining collaborative filtering and sentiment classification for improved movie recommendations. In International Workshop on Multi-disciplinary Trends in Artificial Intelligence (pp. 38–50). Springer Berlin Heidelberg.

    Google Scholar 

  20. Singh, V. K., Mukherjee, M., & Mehta, G. K. (2011). Combining a content filtering heuristic and sentiment analysis for movie recommendations. InComputer Networks and Intelligent Computing (pp. 659–664). Springer Berlin Heidelberg.

    Google Scholar 

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Correspondence to Rajesh Piryani .

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Piryani, R., Gupta, V., Singh, V.K., Ghose, U. (2017). A Linguistic Rule-Based Approach for Aspect-Level Sentiment Analysis of Movie Reviews. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 553. Springer, Singapore. https://doi.org/10.1007/978-981-10-3770-2_19

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  • DOI: https://doi.org/10.1007/978-981-10-3770-2_19

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  • Print ISBN: 978-981-10-3769-6

  • Online ISBN: 978-981-10-3770-2

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