Sentiment Analysis of Movie Reviews Using Hybrid Classification Models
Authors: Sung Min Lee, Hye Jin Kim, Jiwon Park
DOI: 10.87349/JBUPT/27306
Page No: 39-42
Abstract
Internet has provided people a platform to express their opinions and thoughts. Sentiment analysis helps to analyse those opinions and categorize them. This research is done on the movie review dataset obtained from the Internet Movie Database (IMDb). The data is classified using some of the popular learning based classifiers like Naive Bayes, Decision Tree and Support Vector Machine (SVM) classifiers and their accuracies are compared. Finally, the three learning based classifiers are combined using the Majority vote ensemble classifier. It is found that the accuracy obtained from the above said ensemble is better than the individual classifiers and also better than the ensemble which uses the random forest as one of the classifiers.
