Abstract
With the rapid increase in popularity of e-commerce services over the years, all varieties of products are sold online today. Posting online reviews has become a common means for people to express their impressions on any product, while serving as a recommendation for others. To enhance customer satisfaction and buying experience, often the sellers provide a platform for the customers to express their views. Due to the explosion of these opinion rich sites where numerous opinions about a product are expressed, a potential customer finds it difficult to read all the reviews and form an intelligent opinion about the product. In this research, a new framework comprising of the inbuilt packages of python is designed which mines many customers’ opinions about a product and groups them accordingly based on their sentiments, which aids the potential buyers to form a capitalized view on the product. Here classification of the reviews is done using three different classification algorithms i.e. Naïve Bayes Algorithm, Maximum Entropy Classifier and SVM (Support Vector Machine), and their performance is compared. The methodology showcased in this work can be extended easily in all domains.
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References
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)
Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the Web. In: Proceedings of the 14th International Conference on World Wide Web, WWW’05. ACM, New York, USA, pp. 342–351 (2005)
Sharma, R., Nigam, S., Jain, R.: Polarity detection at sentence level. Int. J. Comput. Appl. 86(11) (2014)
Cambria, E., Schuller, B., Xia, Y., Havasi, C.: New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 28(2), 15–21 (2013)
Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. (Production and hosting by Elsevier B.V. on behalf of Ain Shams University.) 5, 1093–1113 (2014)
Khan, S.A., Liang, Y., Shahzad, S.: An empirical study of perceived factors affecting customer satisfaction to re-purchase intention in online stores in China. J. Serv. Sci. Manag. 8, 291–305 (2015)
Khairnar, J., Kinikar, M.: Machine learning algorithms for opinion mining and sentiment classification. Int. J. Sci. Res. Publ. 3(6) (2013)
Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, pp. 417–424 (2002)
Liu, C.L., Hsaio, W.H., Lee, C.H., Lu, G.C., Jou, E.: Movie rating and review summarization in mobile environment. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(3), 397–407 (2012)
Zhu, J., Wang, H., Zhu, M., Tsou, B.K., Ma, M.: Aspect-based opinion polling from customer reviews. IEEE Trans. Affect. Comput. 2(1), 37–49 (2011)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceeding KDD’04 Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM, New York, (2004)
Harb, A., Plantie, M., Dray, G., Roche, M., Trousset, F., Poncelet, P.: Web opinion mining: how to extract opinions from blogs? In: CSTST’08: International Conference on Soft Computing as Transdisciplinary Science and Technology, p. 7 (2008)
Zhang, Q., Wu, Y., Li, T., Ogihara, M., Johnson, J., Huang, X.: Mining product reviews based on shallow dependency parsing. In: SIGIR’09, Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2009)
Fang, X., Zhan, J.: Sentiment analysis using product review data. J. Big Data (a Springer Open Journal) 2(1), 1–14 (2015)
Camelin, N., Bechet, F., Damnati, G., De Mori, R.: Detection and interpretation of opinion expressions in spoken surveys. IEEE Trans. Audio Speech Lang. Process. 18(2), 369–381 (2010)
Krcadinac, U., Pasquier, P., Jovanovic, J., Devedzic, V.: Synesketch: an open source library for sentence-based emotion recognition. IEEE Trans. Affect. Comput. 4(3), 312–325 (2013)
Ekman, P.: Facial expression and emotion. Am. Psychol. 48(4), 384–392 (1993)
Totunoglu, D., Telseren, G., Sagturk, O., Ganiz, M.C.: Wikipedia based semantic smoothing for Twitter sentiment classification. IEEE (2013)
Leskovec, J., Krevl, A.: SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data, June 2014
Finch, A., Sumita, E.: Phrase-based part-of-speech tagging. In: International Conference on Natural Language Processing and Knowledge Engineering, 2007. NLP-KE 2007, Beijing. (Publisher IEEE), pp. 215–220 (2007)
Karim, M., Rahman, R.M.: Decision tree and Naïve Bayes algorithm for classification and generation of actionable knowledge for direct marketing. J. Softw. Eng. Appl. 6, 196–206 (2013)
Nigam, K.: Using maximum entropy for text classification. In: IJCAI-99 Workshop on Machine Learning for Information Filtering, pp. 61–67 (1999)
Hsu, C.W., Chang, C.C., Lin, C.J.: A Practical guide to Support Vector Classification. www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
Continuum Analytics Homepage. https://www.continuum.io/downloads
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Pujari, C., Aiswarya, Shetty, N.P. (2018). Comparison of Classification Techniques for Feature Oriented Sentiment Analysis of Product Review Data. In: Satapathy, S., Bhateja, V., Raju, K., Janakiramaiah, B. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542 . Springer, Singapore. https://doi.org/10.1007/978-981-10-3223-3_14
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DOI: https://doi.org/10.1007/978-981-10-3223-3_14
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