Review Paper: Social Media Sentiment Analysis Using Twitter Dataset

In the rapidly evolving digita lage, social media has emerged as one of the most dominant communication platforms worldwide. Every day, millions of people express their views, emotions, and reactions on various topics, ranging from personal experiences to political movements, product reviews, global events, and social trends. Platforms such as Twitter (now known as X),Facebook, and Instagram serve not only as tools for personal interaction but also as valuable sources of public opinion, crowd behavior, and sentiment-driven insights. Given the sheer volume and velocity of content generated on these platforms, it is humanly impossible to manually monitor or interpret the overall sentimentofusers. Thishasledtothe need for automated sentimentanalysis systems that can extract meaningful patterns and classify emotions in real time. Sentiment analysis, also known as opinion mining, isa powerful technique in the field of natural language processing (NLP) that helps in identifying and categorizing opinions expressed in textual data, especially to determine the writer’s attitude as positive, negative, or neutral..