Abstract
The objective of this article is two-fold. Firstly, a hybrid approach to Sentiment Analysis encompassing the use of Semantic Rules, Fuzzy Sets and an enriched Sentiment Lexicon, improved with the support of SentiWordNet is described. Secondly, the proposed hybrid method is compared against two well established Supervised Learning techniques, Naïve Bayes and Maximum Entropy. Using the well known and publicly available Movie Review Dataset, the proposed hybrid system achieved higher accuracy and precision than Naïve Bayes (NB) and Maximum Entropy (ME).
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References
Anbananthen, K.S.M., Elyasir, A.M.H.: Evolution of opinion mining. Aust. J. Basic Appl. Sci. 7(6), 359–370 (2013)
Appel, O., Chiclana, F., Carter, J.: Main concepts, state of the art and future research questions in sentiment analysis. Acta Polytech. Hung. J. Appl. Sci. 12(3), 87–108 (2015)
Das, S.R., Chen, M.Y., Agarwal, T.V., Brooks, C., Chan, Y.S., Gibson, D., Leinweber, D., Martinez-Jerez, A., Raghubir, P., Rajagopalan, S., Ranade, A., Rubinstein, M., Tufano, P.: Yahoo! for Amazon: sentiment extraction from small talk on the web. In: 8th Asia Pacific Finance Association Annual Conference (2001)
Dzogang, F., Lesot, M.-J., Rifqi, M., Bouchon-Meunier, B.: Expressions of graduality for sentiments analysis - a survey. In: 2010 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1–7 (2010)
Esuli, A., Sebastiani, F.: Senti Word Net - a publicly available lexical resource for opinion mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation (LREC06), pp. 417–422 (2006)
Hatzivassiloglou, V., McKeown, K.: Towards the automatic identification of adjectival scales: clustering adjectives according to meaning. In: Schubert, L.K. (ed.) ACL: Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, pp. 22–26, Ohio State University, Columbus, Ohio, USA, pp. 172–182. ACL, June 1993
Hatzivassiloglou, V., McKeown, K.: Predicting the semantic orientation of adjectives. In: Proceedings of the 35th Annual Meeting of the ACL and the 8th Conference of the European Chapter of the ACL, New Brunswick, NJ, USA. ACL, pp. 174–181 (1997)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings - ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004 full paper), Seattle, Washington, USA, 22–25 August 2004
Kamps, J., Marx, M., Mokken, R.J., de Rijke, M.: Using Word Net to measure semantic orientations of adjectives. In: Proceedings of LREC-04, 4th International Conference on Language Resources and Evaluation, LREC 2004, vol. IV, pp. 1115–1118 (2004)
Liu, B.: Sentiment Analysis and Opinion Mining, Synthesis Lectures on Human Language Technologies, 1st edn. Morgan and Claypool Publishers, San Rafael (2012)
Miller, G.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 81–97 (1956)
Nadali, S., Murad, M., Kadir, R.: Sentiment classification of customer reviews based on fuzzy logic. In: 2010 International Symposium in Information Technology (ITSim), vol. 2, pp. 1037–1040, Kuala Lumpur, Malaysia, June 2010
Pang, B., Lee, L.: 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 (ACL 2005), ACL2005, pp. 115–124 (2005)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. NOW Essence Knowl. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing (EMNLP), vol. 10, pp. 79–86 (2002)
Perkins, J.: Python Text Processing with NLTK 2.0 Cookbook. Packt Publishing, Birmingham (2010)
Potts, C.: Sentiment Symposium Tutorial: Linguistic structure (part of the Sentiment Analysis Symposium held, San Francisco, 8–9 November 2011. Stanford Department of Linguistics, Stanford University (2011). Accessed Dec. 2011
Sadegh, M., Othman, R.I.Z.A.: Combining lexicon-based and learning-based methods for twitter sentiment analysis. Int. J. Comput. Technol. 2(3), 171–178 (2012)
Subasic, P., Huettner, A.: Affect analysis of text using fuzzy semantic typing. IEEE Trans. Fuzzy Syst. 9(4), 483–496 (2001)
Wiebe, J.: Learning subjective adjectives from corpora. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, pp. 735–740. AAAI Press (2000)
Xie, Y., Chen, Z., Zhang, K., Cheng, Y., Honbo, D.K., Agrawal, A., Choudhary, A.N.: Mu SES: a multilingual sentiment elicitation system for Social Media Data. IEEE Intell. Syst. 29(4), 34–42 (2014)
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Appel, O., Chiclana, F., Carter, J., Fujita, H. (2016). A Hybrid Approach to Sentiment Analysis with Benchmarking Results. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_21
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