Evaluating the Effectiveness of Different Machine Learning Approaches for Sentiment Classification

Abstract

This study presents a comparison of four different machine learning algorithms for sentiment analysis on a small subset of the AR-P (Amazon Reviews - Polarity) dataset. The algorithms evaluated are multilayer perceptron (MLP), Naive Bayes, Decision Tree, and Transformer architectures. The results show that the Transformer-based DistilBERT model performed the best with an accuracy rate of 96.10%, while MLP had a better performance than the other remaining methods. Confusion matrices and ROC curves are provided to illustrate the results, and a comparison with previous studies is presented. The study concludes that the results can serve as a basis for future work, such as using larger datasets or comparing the performance of algorithms on different tasks. Overall, this study provides insights into the use of traditional machine learning and modern deep learning methods for sentiment analysis and their potential applications in real-world scenarios.