Sentiment Analysis for Product Review

Sentiment analysis is the technique of using data mining to study, view or analyse sentences in order to anticipate their emotional content using natural language processing .Text is divided into three categories for sentiment analysis: "Positive," "Negative," and "Neutral".It evaluates the data and assigns positive and negative labels to the "better" and "worse" sentiments.As a result, World Wide Web has developed into a vast source of user -or custom-generated raw data in recent years.Users can express their opinions and sentiments by using social media, e-commerce websites, and movie review sites like Facebook, Twitter, Amazon, and Flipkart.In the WWW, where millions of individuals share their opinions in their everyday interactions, whether on social media or in e-mails, which may be their feelings and thoughts about a specific subject.For any kind of decisionmaking process, whether positive or negative, these expanding raw data provide an extraordinarily high supply of knowledge.The field of sentiment analysis has emerged to automatically analyse such massive amounts of data.Sentiment analysis's primary goal is to classify online data by determining its polarity.Although sentiment analysis is a text-based process, it might be difficult to determine a sentence's precise polarity.This asserts that a superior solution must be found in order to outperform any prior method or strategy utilised to determine sentence polarity.Thus, there is a need for automated data analysis approaches to determine the polarity or emotion of a user or client.In addition to a novel strategy that is proposed in this research, a thorough survey of several sentiment analysis methods is included in this paper.