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Research Article | Open Access
Volume 15 2023 | None
SENTIMENT ANALYSIS OF CUSTOMER PRODUCT REVIEW
Mr K KRISHNA REDDY, BEJJANKI PRAGATHI, KANUGU SIDDHARTHA, ALUKARAJAU PRAVEEN, BANDA MANIKANTA,
Pages: 757-765
Abstract
The importance of internet reviews in modern consumer communication and purchasing decisions is undeniable. E-commerce behemoths like Amazon, Flipkart, etc. provide customers a place to voice their opinions and give prospective purchasers honest feedback on how well a product works. This thing may be represented in the form of an occasion, a single blog entry, or a product experience. In this study, we used an Amazon.com dataset that includes customer ratings and comments on digital cameras, computers, smart phones, tablets, televisions, videos, and surveillance systems. Classifying reviews into positive and negative sentiment is necessary for gaining useful insights from a big number of evaluations. Computing research into extracting subjective information from text is known as "sentiment analysis." This research finds that the Product Reviews may be effectively categorized using Machine Learning Techniques. In the study suggested, Sentiment Analysis is used to categorize over 4,000 evaluations into positive and negative categories. Naive Bayes, Support Vector Machine (SVM), and Decision Tree are just some of the categorization methods that have been used to organize reviews. 10Fold Cross Validation is used for the assessment of models.
Keywords
The importance of internet reviews in modern consumer communication and purchasing decisions is undeniable.
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