Abstract
Opinions of customers in ecommerce play a crucial role in day to day usage. Whenever we have to take a decision So the opinions of other individuals plays an important role. There are various social sites and review sites where an user can post their review or opinions towards any products or any issues, So various business and corporate organization wants to know these opinions of user for taking an decision. In ecommerce market, Their is need to analyze the social data or products review automatically, So there is need to create a model which classify the huge amount of product reviews automatically. In this paper we are fetching real time reviews from the social site twitter and apply various text mining techniques to preprocess the data and than apply an machine learning approach through which we can use Naïve Bayes classification algorithm to classify the text into various emotions and polarities.
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Gajbhiye, K., Gupta, N. (2020). Real Time Twitter Sentiment Analysis for Product Reviews Using Naive Bayes Classifier. In: Pandian, A.P., Senjyu, T., Islam, S.M.S., Wang, H. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018). ICCBI 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-24643-3_41
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DOI: https://doi.org/10.1007/978-3-030-24643-3_41
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