Comparative Analysis of Machine Learning Techniques for Sentiment Classification

Abstract—Sentiment analysis is an important part of natural language processing (NLP) and has many applications in social media, e-commerce, and other fields. This study aims to provide a clear distinction between these methodologies by offering a structured overview of sentiment analysis and the variety of techniques used in its execution. The article examines different machine learning algorithms for sentiment analysis and high- lights their advantages and disadvantages by drawing on credible prior research on the subject. Additionally, the study provides a tabular comparison of different machine learning methods by selecting suitable parameters. Keywords: Decision Tree, Support Vector Machine (SVM), Random Forest, Convolution neural network (CNN), Neural network, Long Short-Term Memory Networks (LSTM), BERT