International Journal of Scientific Research and Engineering Development

Social media platforms generate extensive unstructured textual data that reflects public opinions on various topics. AI-based sentiment analysis has become essential for extracting insights from these online conversations, allowing for real-time sentiment monitoring. The field has evolved from lexicon-based and classical machine learning methods to advanced deep learning techniques, particularly utilizing large language models (LLMs) like BERT and GPT-series models. These models effectively capture contextual nuances, making them superior for analyzing noisy social media content. By 2025–2026, implementations often involve fine-tuned transformers and hybrid systems that enhance accuracy and efficiency. Applications include brand health tracking, crisis detection, and public health surveillance. Despite advancements, challenges such as sarcasm detection, bias in training data, and multilingual performance persist. Overall, AI sentiment analysis has transformed into a sophisticated tool that aids various stakeholders in understanding the digital society, with a future focus on multimodal, ethically aligned approaches. The findings underscore practical value in real-time applications such as brand monitoring, crisis detection, public opinion tracking, and trend analysis. However, persistent challenges include sarcasm/irony detection, multimodal integration, dataset bias, and low-resource language support. Future research should prioritize Indic-specific fine-tuning, real-time multimodal processing, explainability enhancements, and ethical safeguards to enable more robust, inclusive, and impactful sentiment analysis in diverse digital ecosystems.

Ragul R N, J.Savitha,"AI Based Sentimental Analysis for Social Media" International Journal of Scientific Research and Engineering Development, V9(1): Page(2174-2179) Jan-Feb 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.