Abstract
While buying a product on an e-commerce website, a consumer has to face the inevitable question whether the product which he is buying is the best option or not. With the exponential growth in the number of e-commerce sites and the products that they are selling, the customer has to identify the most optimally useful product. He has to choose the product that is best fit and gives value for the money. The previous advances used for analyzing whether a product is good or bad uses traditional sentiment analysis methods performing operations on the ratings and reviews submitted by a customer after purchase, whose accuracy lies in the lower half of the graph. Apart from giving comparatively low accuracy percentage, these previous approaches used bipolar classification, which means that the products were classified as good or bad without acknowledging the fact that a product can be average or just okay.
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Sarkar, S., Palit, S. (2020). Sentiment Analysis of Product Reviews of Ecommerce Websites. In: Mathur, G., Sharma, H., Bundele, M., Dey, N., Paprzycki, M. (eds) International Conference on Artificial Intelligence: Advances and Applications 2019. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-1059-5_7
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DOI: https://doi.org/10.1007/978-981-15-1059-5_7
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