Natural Language Processing for Sentiment Analysis in Socialmedia Techniques and Case Studies

Social media platforms have become a significant medium for expressing opinions, emotions, and sentiments, making sentiment analysis a crucial task in Natural Language Processing (NLP). While various sentiment analysis techniques have been proposed, existing studies often face challenges such as language dependency, platform-specific biases, lack of real-time processing, and limited multimodal analysis. This research explores the evolution of sentiment analysis in social media by leveraging cutting-edge NLP techniques, including transformer-based models (BERT, RoBERTa, GPT) and multimodal approaches. By addressing the limitations of previous studies, our research proposes a real-time, multilingual, and cross-platform sentiment analysis model capable of analyzing textual, audio, and visual content from diverse social media platforms (e.g., Twitter, Facebook, Instagram, and TikTok). Additionally, this study investigates the effectiveness of domain-specific sentiment analysis (e.g., political discourse, health-related discussions) to improve sentiment classification in specialized contexts. Benchmark datasets and experimental validation will be used to compare existing sentiment analysis models with our proposed approach. Our findings aim to enhance scalability, accuracy, and real-time adaptability of sentiment analysis in social media applications, ultimately contributing to improved decision-making in social monitoring, brand analysis, and crisis management.