• Pratham Kolhe
• Parag Kulkarni
• Amay Mapari
• Mugdha Malpe
• Abhi Deshmukh

Digital reviews play a crucial role in shaping consumer behavior and global communication. This research paper outlines a systematic approach to enhance product ratings using sentiment analysis. Utilizing classification models like Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbour, the study categorizes customer reviews into predefined positive or negative sentiment categories. The resulting ratings, on a scale of 1 to 5, provide prospective customers with a clear understanding of product reviews, aiding their decision-making process. This approach bridges the gap between textual data and meaningful product evaluations in the ever-growing digital marketplace.

Sentiment Analysis, Support Vector Machine, Naive Bayes, K-NearestNeighbour, Vector Representation, Polarity Detection, Product Rating.

"Sentiment Analysis Of Customer Reviews For Product Rating", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.a391-a395, March-2024, Available :http://www.jetir.org/papers/JETIR2403050.pdf

"Sentiment Analysis Of Customer Reviews For Product Rating", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppa391-a395, March-2024, Available at : http://www.jetir.org/papers/JETIR2403050.pdf