Product Based Sentiment Analysis Using ML

With the rapid rise of e-commerce, a big number of products are being sold online, and a growing number of people are making purchases online. People provide feedback on products purchased in the form of reviews when shopping. User-generated product and service reviews are widely available on the internet. We employ emotive analysis to extract the required info from the vast amount of material available on the internet. Sentiment analysis uses the notion of natural language processing to extract abstract and to-the-point information for source materials. It is used to deal with the identification and consolidation of client feedback. These reviews are extremely important in assessing potential customers for products as well as market trends. This document presents an overview of product reviews by categorizing them as good, negative, or neutral. The amount of information available on the internet is enormous. Because reviews are highly unstructured, machine learning algorithms such as nave Bayes and support vector machine algorithms are used. These algorithms take unstructured product reviews as inputs, perform preprocessing, calculate polarity of reviews, extract features on which comments are made, and plot a graph for the result. Finally, the algorithms' precision, recall, and accuracy are assessed.