Journal of Intelligent Computing Phuong Nguyen, Van-Huu Tran, The-Bao Nguyen, Hung Ho-Dac 16 3 2025 https://doi.org/10.6025/jic/2025/16/3/103-113 https://www.dline.info/jic/fulltext/v16n3/jicv16n3_2.pdf Sentiment analysis is a field of study in natural language processing (NLP). This study proposes an approach to data processing, feature extraction, data balancing, and training using four machine learning models: Multinomial Naïve Bayes, Random Forest, Support Vector Machine, and Decision Tree. Firstly, the dataset selected in the paper comprises the Internet Movie Database (IMDb), Twitter US Airline Sentiment (US Airline), and SemEval 2017. Second, data processing, feature extraction, and data balancing are employed to improve the accuracy of the training dataset. Specifically, data balancing is performed using the K-means SMOTE method, which has been proven effective for classification. Finally, the standard feature sets are applied to four machine learning models for training. The experimental results indicate that the SVM model achieves the highest accuracies of 89%, 96%, and 75% on the IMDb, US Airline, and SemEval 2017 datasets, respectively, compared to other state-of-the-art models.