Sentiment Analysis Based on Machine Learning Techniques: A Comprehensive Review

Abstract

In the landscape of digital communication, sentiment analysis stands out as a pivotal technology for deciphering the vast troves of unstructured text generated online. When integrated with machine learning, sentiment analysis transforms into a powerful tool capable of distilling insights from complex human emotions and opinions expressed across social media, reviews, and forums. This review paper embarks on a thorough exploration of the integration of machine learning techniques with sentiment analysis, shedding light on the latest advancements, challenges, and applications spanning various sectors including public health, finance, and consumer behavior. It meticulously examines the role of machine learning in elevating sentiment analysis through improved accuracy, adaptability, and depth of analysis. Furthermore, the paper discusses the implications of these technologies in understanding consumer sentiment, tracking public health trends, and forecasting market movements. By synthesizing findings from seminal studies and cutting-edge research, this review not only charts the current landscape but also forecasts the trajectory of sentiment analysis. It underscores the necessity for ongoing innovation in machine learning models to keep pace with the evolving digital discourse. The insights presented herein aim to guide future research endeavors, highlight the transformative impact of machine learning on sentiment analysis, and outline the potential for new applications that could benefit society at large.