Abstract
Understanding client conclusions is of high significance in advertising systems today. Not exclusively will it give organizations an understanding on how clients see their items as well as administrations, yet it will likewise give them an idea en route to work on their offers. This paper endeavors to know the connection of different factors in client surveys on women clothing Internet business information, and to classify every review whether it recommends the product to buy or not and if it consists of positive or negative or neutral sentiment. To understand these objectives, we utilized univariate and multivariate examinations on dataset includes separated from survey messages, which we carried out NLP procedures for suggestion and feeling arrangement. Results have shown that a suggestion might be a solid pointer of a positive opinion score and the other way around. Then again, appraisals in item surveys are fuzzy pointers of sentiment scores. We likewise discovered that the multinomial naïve Bayes was prepared to arrive at a F1-score of 0.9596 for sentiment classification and 0.928355 for opinion arrangement.
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Manikiran, G., Greeshma, S., Vishnu Teja, P., Sreehari Rao, Y., Sardar, T.H., Mallik, M.A. (2022). E-commerce Clothing Review Analysis and Model Building. In: Reddy, A.B., Kiranmayee, B., Mukkamala, R.R., Srujan Raju, K. (eds) Proceedings of Second International Conference on Advances in Computer Engineering and Communication Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-7389-4_48
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DOI: https://doi.org/10.1007/978-981-16-7389-4_48
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