Advancements and Comparative Analysis of Opinion Mining Techniques: A Review of Methods and Algorithms

Opinion Mining, often known as Sentiment Analysis, is a heavily researched area in computer linguistics and natural language processing. It is a difficult and developing field because a lot of study has been done on various sentiment analysis methodologies and approaches. This paper seeks to offer a succinct review of sentiment analysis technology, covering numerous techniques, algorithms, and tools. Additionally, it analyzes and compares the results of sentiment analysis research conducted in various countries. The study also identifies topics in the field of sentiment analysis that need to be explored further for solutions to present problems and unmet research requirements. The paper emphasizes the significance of understanding the trade-offs between lexicon-based, machine learning, and deep learning approaches in sentiment analysis crucial for selecting the appropriate technique. Comparing different techniques of sentiment analysis is important to determine which technique is best suited for a specific use case or dataset, to identify areas for improvement, and to make data-driven decisions based on accurate and reliable sentiment analysis highlighting their potential for improving accuracy, scalability, and adaptability in sentiment classification tasks.