Abstract
Online opinions have turn out to be a critical supply of data for customers earlier than making a knowledgeable buy rule. Rapid product opinions have a tendency to keep an excessive effect on the following product sales. We take the drive to examine the conduct traits of the early analysis via they have published opinions on universally huge e-trade tenets, namely Amazon and Yelp. In our project, we had selected to paintings on reading opinions of the diverse online product that has been reviewed in the shape of texts and the feature additionally been given a score on a scale from 1 to 5. We had received these statistics have set which had 2 statistics to be set: educate and check (break up as 75–25%). We had broken up the range score for the product into instructions in general: positive, negative, thereby producing the accuracy of the graph among the three algorithms of machine learning based on the reviews of the product given by the customer for the online products.
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Samba Siva Rao, C., Hema Naga Padma, K., Lakshmi, G., Laya, C., Harika, A. (2022). Sentiment Review Analysis and Text Summarization Using Supervised Machine Learning Algorithms. In: Pandian, A.P., Palanisamy, R., Narayanan, M., Senjyu, T. (eds) Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-7330-6_68
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DOI: https://doi.org/10.1007/978-981-16-7330-6_68
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