Sentiment Analysis: Methods, Applications, and Future Directions

Abstract

Abstract: Sentiment analysis is a rapidly evolving field that aims to automatically identify and extract subjective information from text data. In recent years, sentiment analysis has gained widespread attention due to its potential applications in various domains, such as marketing, social media analysis, and customer feedback analysis. In this review paper, we provide a comprehensive analysis of sentiment analysis techniques, including traditional rule-based methods, machine learning-based methods, and deep learning-based methods. We discuss the advantages and limitations of these methods and compare their performance in various settings. Furthermore, we examine the challenges and opportunities in sentiment analysis research and present future directions for the field. Overall, this review aims to provide a critical assessment of sentiment analysis techniques, applications, and future developments, and to assist researchers and practitioners in understanding the state-of-the-art in this important area of natural language processing