Published Paper Details:

With the exponential growth of social media platforms such as Twitter, Facebook, Instagram, and Reddit, vast amounts of user-generated content are produced every second. Sentiment analysis, also known as opinion mining, is the computational study of people's opinions, sentiments, attitudes, and emotions expressed in text. This paper presents a comprehensive review of sentiment analysis techniques applied to social media data, encompassing lexicon-based methods, machine learning algorithms, and state-of-the-art deep learning architectures. We examine the unique challenges posed by social media text -- including informal language, sarcasm, emojis, code-mixing, and data imbalance -- and survey benchmark datasets commonly used in the field. Furthermore, we explore real-world applications spanning business intelligence, political analysis, public health monitoring, and disaster management. Experimental comparisons of leading models are discussed, and open research directions are identified. Our study demonstrates that transformer-based models such as BERT and RoBERTa currently achieve the highest performance, while hybrid approaches combining linguistic knowledge with neural networks show promising potential.