Sentiment Analysis of Online Customer Feedbacks Using NLP and Supervised Learning Algorithm - Anbazhagan, Kannagi; Singhal, Priyank; Gupta, Mukur; Saxena, Kumud (International Journal of Intelligent Systems and Applications in Engineering, 2023)
E-Commerce is now an integral part of our everyday lives because of the proliferation of computers and the lightning-fast growth of the Internet. Numerous reviews are available for widely used products. In addition to making it harder for consumers to make an educated purchase choice, this also makes it harder for the product’s producer to monitor and respond to customer feedback. One of the fastest-growing subfields in social media analysis is sentiment analysis and opinion mining. It’s crucial since it gives companies a chance to concentrate on enhancing the company plan and learn more about the comments buyers have about their product. This research suggests using sequential Bayesian linear regression (SBLR) for Sentiment analysis of electronic customer comments. It includes mining a person’s thoughts and feelings about a firm via computer analysis of customer purchasing patterns. The dataset used in this article has been gathered from Amazon and consists of customer opinions on various electrical products. We used machine learning (ML) techniques after analyzing the reviews to determine whether they were favorable or negative. The findings of this article indicate that ML Techniques provide the best outcomes for categorizing online Reviews.