SENT2BAYES: A Hybrid Machine Learning Model Combining Word2vec and Multi-Nomial Naive Bayes Classifier for Movie Review Sentiment Analysis in Twitter
J. Nirmal Jothi, N. Soundiraraj, P. Ebby Darney, R. Santhana Krishnan, K. Lakshmi Narayanan, S. Sundararajan
Abstract
Movie review sentiment analysis has gained significant attention due to the vast amount of user-generated content available on social media platforms like Twitter. Accurately analyzing sentiment from such data can provide valuable insights into public opinion and assist in decision-making processes. In the current work, a novel machine learning model, called Sent2Bayes, for movie review sentiment analysis on Twitter is proposed. The proposed model combines the power of Word2Vec embeddings and the Multinomial Naive Bayes classifier to enhance sentiment prediction accuracy. The Sent2Bayes model begins with the creation of Word2Vec embeddings, which capture semantic relationships between words and encode contextual information. These embeddings are then fed into the Multinomial Naive Bayes classifier, which leverages probabilistic modelling to predict sentiment labels for movie reviews. By combining the strength of Word2Vec's semantic understanding and Multi-nomial Naive Bayes' probabilistic approach, Sent2Bayes aims to improve the accuracy and robustness of sentiment analysis on Twitter data. To evaluate the performance of Sent2Bayes, this study conducts an extensive simulation experiments comparing it with existing algorithms commonly used for sentiment analysis. Further, this study employs suitable simulation metrics, including accuracy, precision, recall, and confusion matrix, to measure the model's predictive capabilities and generalization ability. The simulation results demonstrate that Sent2Bayes achieves superior sentiment analysis performance compared to SVM and RNN. The proposed model shows higher accuracy, precision, recall, and confusion matrix across various datasets and test scenarios. The improved performance of Sent2Bayes effectively captures contextual information through Word2Vec embeddings and the robust probabilistic modelling of Multinomial Naive Bayes.