Abstract
Online shopping websites and the people using the Online shopping websites are proliferating every day. The widely available internet resources are letting the users to shop any products anywhere, anytime at any cost. With the brisk development in the 3G and 4G we can expect a tremendous development in the area of M-commerce and E-commerce. In this paper, we have presented our work which is an extension to our earlier work which is the comparison of two mobile products based on predefined score and features of the Mobile. Therefore, we have shown in this paper the ranking of products, ranking of products based on features, comparison of websites Flipkart and Amazon, comparison of algorithms Naive Bayes classifier, decision tree classifier and Maximum Entropy classifier based on accuracy which is used in the classification of reviews. Finally, we have shown these rankings in a graphical user interface (GUI) to recommend the user the best product.
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Rajeev, P.V., Rekha, V.S. (2016). Opinion Mining of User Reviews Using Machine Learning Techniques and Ranking of Products Based on Features. In: Suresh, L., Panigrahi, B. (eds) Proceedings of the International Conference on Soft Computing Systems. Advances in Intelligent Systems and Computing, vol 398. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2674-1_59
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DOI: https://doi.org/10.1007/978-81-322-2674-1_59
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