COMPARISON OF NAIVE BAYES AND RANDOM FOREST METHODS IN SENTIMENT ANALYSIS ON THE GETCONTACT APPLICATION

Abstract

The rapid growth in the use of social media and instant messaging platform apps has significantly changed the way people communicate. One of the most popular apps is GetContact, a platform focused on identifying the phone numbers of irresponsible people and reducing the impact of spam calls. In cases like this, sentiment analysis is important to understand user responses to the service. In performing sentiment analysis, there are two classification methods that will be used, namely the Naive Bayes and Random Forest methods. This research utilizes the SMOTE technique to handle data imbalance, and the results show that the application of SMOTE successfully improves classification accuracy. The Random Forest model performed better than Naive Bayes, with 80% accuracy, 84% precision, 77% recall, and 80% F1 score for positive sentiments, while Naive Bayes achieved 77% accuracy, 79% precision, 79% recall, and 79% F1 score. Although Random Forest is superior in precision, recall , and F1 score for positive sentiments, it performs almost on par with Naive Bayes in classifying negative sentiments, with 76% precision , 84% recall, and 80% F1 score for Random Forest, and 76% precision, 76% recall , and 76% F1 score for Naive Bayes. This shows that both models provide similar results in identifying negative sentiment overall.