Sentiment Analysis Using Machine Learning

Abstract

Sentiment analysis has changed completely in 2024, with developments in deep learning and machine learning, and multimodal approaches. This paper reviews ten recent studies that explore various sentiment analysis techniques, including transformer-based models (GPT-4, LLAMA 3, FinBERT), conventional techniques for machine learning (Nave Bayes, Logistic Regression), and multimodal frameworks integrating text and images. The findings suggest that large language models (LLMs) perform well in Learning situations with zero-shot and few-shot but struggle with complex sentiment understanding. Traditional models like Logistic Regression remain competitive in financial sentiment prediction, while multimodal approaches such as M2SA and topic-oriented models excel in image-text sentiment analysis. This review highlights The advantages and disadvantages of different techniques, supplying information for researchers and practitioners in choosing the most suitable approach for their particular uses.