Abstract:
Sentiment analysis is a recent area of research where vast amounts of data are analyzed to
offer insightful information about a particular subject. It is a strong instrument that can
benefit organizations, customers, and even governments. Nowadays, analyzing the
emotional content of movie reviews is popular. The approach heavily relies on textual
emotion recognition. Analysts in Machine Learning (ML) and Natural Language
Processing (NLP) have investigated a variety of approaches to execute the procedure with
the highest level of accuracy. There are three stages to the sentiment analysis procedure for
movie reviews. We must first gather reviews from online platforms. The collected data will
then be analyzed. Finally, we will have all of the data that has been processed regarding
the tone of those reviews. The results of the model analysis may aid the consumer in
understanding how viewers feel about a certain film. To conduct this research, movie
reviews were subjected to sentiment classification methods. For review sentiment
classification, we looked at SVM, Naive Bayes, Logistic Regression, and K-NN, four
supervised machine learning methods. Empirical results with a large number of reviews in
the training dataset show that the SVM model performs better than the Naive Bayes,
Logistic Regression, and K-NN approaches. The SVM method achieved an 87.46%
accuracy, an 87.21% precision rate, and an 87.46% recall rate.
