Authors: Mr. Satish Yadav, Ashish Khandagale, Dr. Jasbir Kaur, Ms. Sandhya Thakar, MS. Ifra Kampoo
Abstract: The exponential growth of social media platforms has generated unprecedented volumes of user-generated content, creating vast repositories of public opinion and sentiment. This research investigates the application of sentiment analysis techniques to social media big data, examining methodologies for extracting, processing, and analyzing emotional insights from large-scale social media datasets. Through a comprehensive review of machine learning approaches, natural language processing techniques, and big data analytics frameworks, this study evaluates the effectiveness of various sentiment classification models when applied to Twitter, Facebook, and Instagram data. Our findings demonstrate that hybrid approaches combining lexicon-based methods with deep learning architectures achieve superior accuracy rates of 89.3% compared to traditional rule-based systems. The research also addresses critical challenges including data preprocessing, feature engineering, and scalability issues inherent in social media sentiment analysis. The implications of this work extend to business intelligence, political analysis, brand monitoring, and public health surveillance applications.
