Comparison of Naive Bayes and SVM Classification in Grid-Search Hyperparameter Tuned and Non-Hyperparameter Tuned Healthcare Stock Market Sentiment Analysis

This paper compares the performance of Naive Bayes and SVM classifiers classification based on sentiment analysis of healthcare companies' stock comments in Bursa Malaysia. Differing from other studies which focus on the performance of the classifier models, this paper focuses on identifying the hyperparameters of the classifier models that are significant for sentiment analysis and the optimization potential of the models. Grid Search technique is used for the hyperparameters tuning process. The performance such as precision, recall, f1-score, and accuracy of Naive Bayes and SVM before and after hyperparameter tuning are compared. The results show that the important hyperparameters for Naive Bayes are alpha and fit_prior, while the important hyperparameters for SVM are C, kernel, and gamma. After performing hyperparameters tuning, SVM gave a better performance with an accuracy of 85.65% than Naive Bayes with an accuracy of 68.70%. It also proves that hyperparameter tuning is able to improve the performance of both models, and SVM has a better optimization potential than Naive Bayes.