Sentiment Analysis of Customer Reviews Based Texts Using Classification Algorithms

Nowadays, a large number of text-based sentiments posted by customers about their reviews on products. They provide their sentiments via their reviews. Sentiment analysis is also known as an opinion mining. Businesses frequently do sentiment analysis on textual data to track the perception of their brands and products in customer reviews and to better understand their market. In this research work, it is implemented that the Naive Bayes, Random Forest, Decision Tree and Support Vector Machines classifiers and the results are compared and examined. This work has a performance-based analysis of different classifiers by assessing the accuracy of classification depending on the size of the product data sets of mobile products. The dataset which has been collected from the shopping websites of Amazon, Flipkart, Snapdeal and which is used to find the classification accuracy by its methods used for the analysis. Finally, it compares the performance of four classification methods like Naive Bayes, Random Forest, Decision Tree and Support Vector Machines to identify the best method.