Paper Details

Name: Pragya Rajput, Aditya Jain, Lovish Gupta & Abhishek Thakur
Country: India

Year: 2026
Volume: Volume No: 13, January, Year: 2026 (Special Issue)
Page Number: 177-184
DOI: https://doi.org/10.5281/zenodo.18975651

ABSTRACT—
Sentiment analysis is one of the important research fields in natural language processing, which is currently gaininga lot of significance due to the increasing growth of social media. This paper provides an overall review of the techniques concerning sentiment analysis, strictly developed for social media contexts. We will discuss a wide variety of methodologies, from classical statistical methods to state-of-the-art machine learning and deep learning methods. It discusses preprocessing methods, feature extraction strategies, and model evaluation metrics by outlining implications for effective sentiment detection. Further, it outlines some of the key applications of sentiment analysison social media platforms, such as brand management, public opinion monitoring, and crisis management. Current challenges such as dealing with noisy data, handling ambiguity in sentiment, and ensuring model generalization across diverse social media platforms are identified. Finally, we discuss some emerging trendsin sentiment analysis research and future directions, among them the integration of multimodal
data and the application of transfer learning. This review intends to provide a holistic viewof sentiment analysis techniques and their practical applications, thus offering insights into important areas for both researchers and practitioners in the field.

Index Terms—Sentiment Analysis, Social Media, Machine Learning, Natural Language Processing, Feature Extraction