Comparative Analysis of Machine Learning Techniques for Sentiment Classification

Comparative Analysis of Machine Learning Techniques for Sentiment Classification

Prof. P.J. Jambhulkar, Tejasvi Alur, Ritanshu Koul, Khushi Gandhi, Raj Nathani

Deparment of Computer Engineering, Pune Institute of Computer Technology pjjambhulkar@pict.edu, tejasvi.alur@gmail.com, ritanshu.koul@gmail.com, khushigandhi1706@gmail.com, rajnathanim@gmail.com

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