A Comparative Study of Traditional Machine Learning and Deep Learning Techniques for Sentiment Analysis

Abstract

Sentiment analysis plays a vital role in understanding user opinions across digital platforms. This paper presents a comparative study between traditional machine learning algorithms (such as Naïve Bayes and Support Vector Machines) and deep learning models (including RNN and transformers). The analysis evaluates performance based on accuracy, computational complexity, and scalability. Results show that deep learning models outperform traditional methods in large datasets, while simpler models remain effective for resource-constrained environments.