Abstract
Social media has evolved into a highly strong means of communication between individuals, allowing them to express their opinions and ideas in each conversation or article, resulting in a massive volume of unstructured data. Organizations must process and research the data as well as gather business information in order to analyze it. In this article, machine learning models such as Multinomial Naive Bayes, Logit Regression, Linear Support Vector Classifier SVC, and Multinomial Random Forest are used to analyze Amazon's product reviews. We conducted a comparison examination of these models by implementing them and deciding which model detects the polarity of sentiments with the greatest accuracy, and we discovered that Logit Regression and Linear SVC both perform well, with 87.3% and 87.4% accuracy, respectively. To summarize, the purpose of this study is to do a comparison analysis in order for future researchers to choose the best algorithm for their research.
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Tufchi, S., Yadav, A., Rai, V.K., Banerjee, A. (2023). Sentiment Analysis on Amazon Product Review: A Comparative Study. In: Khanna, A., Polkowski, Z., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes in Networks and Systems, vol 572. Springer, Singapore. https://doi.org/10.1007/978-981-19-7615-5_13
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DOI: https://doi.org/10.1007/978-981-19-7615-5_13
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