An Approach for Sentiment Analysis using Balanced Learning
Phuong Nguyen, Van-Huu Tran, The-Bao Nguyen, Hung Ho-Dac
Journal of Intelligent Computing, Vol. 16, No. 3, 2025.
DOI: https://doi.org/10.6025/jic/2025/16/3/103-113
Link: https://www.dline.info/jic/fulltext/v16n3/jicv16n3_2.pdf
Abstract:
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 NaÃƒÂ¯ve 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.