Abstract

There are now a great deal of consumer reviews of items that are written entirely in text. The reviews express their opinions. Opinion mining is another name for sentiment analysis. A common way for businesses to keep tabs on how customers feel about their brands and goods is through the use of sentiment analysis on textual data. Naive Bayes, Random Forest, Decision Tree, and Support Vector Machine classifiers are all used and compared in this study. In this study, we evaluate the efficacy of several classifiers by measuring their ability to correctly categorize mobile product data sets of varying sizes. Data were collected from popular online retailers like Amazon, Flipkart, and Snapdeal and analyzed to determine categorization accuracy. Naive Bayes, Random Forest, Decision Tree, and Support Vector Machines are some of the categorization algorithms compared here.