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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.
Sentiment Analysis, Movie Review, Ensemble classifier, Majority Vote Classifier.
Sentiment Analysis, Movie Review, Ensemble classifier, Majority Vote Classifier.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 7 |

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