Comprehensive Overview of Sentimental Analysis by Utilizing Machine Learning Techniques

Abstract

Objective: This Comprehensive review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to comprehensively examine the landscape of sentiment analysis techniques utilizing machine learning algorithms and artificial intelligence (AI) approaches.

Background: Sentiment analysis has emerged as a critical domain in natural language processing. It leverages advanced computational techniques to extract and interpret emotional nuances from textual data across diverse domains, including social media, customer feedback, market research, and political discourse.

Methodology: A comprehensive systematic review was conducted following the PRISMA 2020 guidelines. Multiple academic databases, including Web of Science, Scopus, IEEE Xplore, and ACM Digital Library, were systematically searched using predefined inclusion and exclusion criteria. The search strategy employed a combination of keywords: "sentiment analysis," "machine learning," "artificial intelligence," "natural language processing," and related terms.

Conclusion: The systematic review reveals significant advancements in sentiment analysis through machine learning and AI technologies. The emerging techniques demonstrate remarkable potential in understanding and interpreting human emotions across diverse textual landscapes, with continuous improvements in accuracy, interpretability, and domain adaptability.