International Journal of Science, Engineering and TechnologyAuthors: Ms. Ruchika Kadu, Ms. Vaishnavi Nawle, Mr. Jayesh Bisane, Mr. Nikhil Barapatre
Abstract: The exponential growth of social media platforms has fundamentally transformed the way individuals communicate, share opinions, express emotions, and influence public discussions across the world. Platforms such as X (formerly Twitter), Facebook, Instagram, Reddit, LinkedIn, and YouTube generate billions of user-generated posts, comments, reviews, and discussions every day. These digital interactions represent a valuable source of information that reflects people's attitudes, emotions, preferences, experiences, and behavioral patterns regarding products, services, political events, healthcare, education, entertainment, and social issues. As organizations increasingly rely on data-driven decision-making, extracting meaningful knowledge from this vast amount of unstructured textual data has become an important research area. Sentiment Analysis, also known as Opinion Mining, has emerged as one of the most effective Artificial Intelligence (AI) techniques for automatically identifying and classifying emotions, opinions, and attitudes expressed in textual content. Sentiment Analysis integrates Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL), and computational linguistics to analyze textual information and determine whether a particular opinion is positive, negative, or neutral. Unlike traditional data analysis methods that mainly focus on structured numerical information, sentiment analysis enables organizations to interpret human emotions and understand public perceptions from unstructured social media content. This capability provides significant advantages in understanding customer satisfaction, monitoring brand reputation, identifying emerging trends, predicting market behavior, and supporting strategic decision-making. The increasing adoption of Artificial Intelligence has further enhanced the accuracy and scalability of sentiment analysis systems, making them capable of processing millions of social media posts in real time.
