A Comprehensive Review of Sentiment Analysis: Techniques, Datasets, Limitations, and Future Scope

Sentiment analysis uses opinions from the public to analyze how people feel about a variety of social, political,and cultural issues. The review commences by outlining the fundamental concepts, significance, and approaches of sentiment analysis in comprehending viewpoints, feelings, and subjectivity contained within the text. This study considers two main types of techniques: machine learning (ML), deep learning (DL), and ensemble learning (EL) to overcome existing limitations. By analyzing their effectiveness in decoding emotional complexities and achieving accurate predictions, this review provides insights on their utility. Additionally, it emphasizes commonly used datasets, serving as benchmarks to assess model robustness. With the aim of influencing future breakthroughs in sentiment analysis, this research contributes to the ongoing refinement of this dynamic field. Lastly, we have addressed some research challenges and outlined future directions, providing valuable insights for readers seeking to advance sentiment analysis methodologies.