Feature-Level Sentiment Analysis of Data Collected through Electronic Commerce

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When dealing with data in the form of text, the most popular method for doing analysis and determining sentiment content is called "Sentiment Analysis." Sentiment analysis is also known as Opinion Mining. Suggestions, feedback, tweets, and comments are all examples of the various types of text data that are being created. Customer feedback on e-commerce sites is a constant source of new information. Online stores may better meet client needs, improve their services, and boost sales by analyzing E-Commerce data. Positive, negative, and neutral feedback from customers may be separated using sentiment analysis. Numerous methods for Sentiment Analysis have been developed by academics. Typically, only a single machine learning algorithm is used for sentiment analysis. The purpose of this study, which makes use of Amazon review data, is to extract positive, negative, and neutral review ratings by locating aspect phrases, identifying the Parts-of-Speech, and applying classification algorithms to the collected data.