Effective Sentimental Analysis and Opinion Mining of Web Reviews Using Rule Based Classifiers

Abstract

Sentiment Analysis is becoming a promising topic with the strengthening of social media such as blogs, networking sites etc. where people exhibit their views on various topics. In this paper, the focus is to perform effective Sentimental analysis and Opinion mining of Web reviews using various rule based machine learning algorithms. we use SentiWordNet that generates score count words into one of the seven categories like strong-positive, positive, weak-positive, neutral, weak-negative, negative and strong-negative words. The proposed approach is experimented on online books and political reviews and demonstrates the efficacy through Kappa measures, which has a higher accuracy of 97.4 % and lower error rate. Weighted average of different accuracy measures like Precision, Recall, and TP-Rate depicts higher efficiency rate and lower FP-Rate. Comparative experiments on various rule based machine learning algorithms have been performed through a Ten-Fold cross validation training model for sentiment classification.