Abstract
Over the past decade, Indian e-commerce sector witnessed a huge growth. Currently this industry has approximately 40 million customers and it is expanding. These people express their experiences with various products, services in several websites, blogs, and social networking sites. To identify and extract any subjective knowledge from these huge unstructured user data, we need to develop a method that can collect, analyze, and classify user opinions. Two popular learning techniques (Supervised and Unsupervised) can be used to classify an opinion into two classes—“Positive” or “Negative.” In this paper, we propose an integrated framework for product review collection and unsupervised classification. The categorization of reviews is generated by the average semantic orientation of the phrases of suggestions or opinions in the review that holds adjectives as well as adverbs. A review can be categorized as an “Endorsed” one when the average semantic orientation is “Positive” otherwise it is an “Opposed” (“Negative”) one. Our proposed method has been tested on some real-life datasets collected from an Indian e-commerce website. The experimental results obtained show the efficiency of our proposed method for classification of product reviews.
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References
PWC, ASSOCHAM: Evolution of e-commerce in India Creating the bricks behind the clicks (2014)
Brill, E.: Some advances in transformation-based part of speech tagging. In: Proceedings of the Twelfth National Conference on Artificial Intelligence, pp. 722–727. AAAI Press (1994)
Turney, P.D.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on association for computational linguistics, pp. 417–424. Association for Computational Linguistics (2002)
Hatzivassiloglou, V., McKeown, K.R.: 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, pp. 174–181. Association for Computational Linguistics (1997)
Turney, P.D., Littman, M.L.: Measuring praise and criticism: inference of semantic orientation from association. ACM Trans. Inform. Syst. (TOIS) 21(4), 315–346 (2003)
Ding, X., Liu, B.: The utility of linguistic rules in opinion mining. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 811–812. ACM (2007)
Takamura, H., Inui, T., Okumura, M.: Extracting semantic orientations of words using spin model. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 133–140. Association for Computational Linguistics (2005)
Tong, R.M.: An operational system for detecting and tracking opinions in on-line discussion. In: Working Notes of the ACM SIGIR 2001 Workshop on Operational Text Classification, vol. 1, p. 6 (2001)
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-Volume 10, pp. 79–86. Association for Computational Linguistics (2002)
Kamps, J., Marx, M.J., Mokken, R.J., De Rijke, M.: Using wordnet to measure semantic orientations of adjectives (2004)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 168–177. ACM (2004)
Liu, B.,Zhang, L.: A survey of opinion mining and sentiment analysis. In: Mining text data, pp. 415–463. Springer, US (2012)
Russell, D.: AROUND has always been around. http://searchresearch1.blogspot.in/2010/10/around-has-always-been-around.html. Accessed Mar 5 2015. 8.00 PM IST
Taboada, M., Anthony, C., Voll, K.: Methods for creating semantic orientation dictionaries. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC), pp. 427–432 (2006)
Acknowledgments
This paper is an outcome of the work carried out for the project titled “In search of suitable methods for Clustering and Data mining” under “Mobile Computing and Innovative Applications programme” under the UGC funded—University with potential for Excellence—Phase II scheme of Jadavpur University.
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Barman, D., Tudu, A., Chowdhury, N. (2016). Opinion Classification Based on Product Reviews from an Indian E-Commerce Website. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 380. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2523-2_69
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DOI: https://doi.org/10.1007/978-81-322-2523-2_69
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