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@article{169342,
        author = {Varun Pratap and Shiv Sharan Dixit and Arpit Negi and Himani Aggarwal and Shubham Kumar and Anupam Sharma},
        title = {AI Sentiments Analysis for Social Media},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1203-1210},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169342},
        abstract = {With the rapid proliferation of social media platforms, vast amounts of user-generated content are produced daily, providing valuable insights into public opinions and sentiments. AI-driven sentiment analysis systems, leveraging deep learning techniques, offer a powerful tool to analyze this data, allowing businesses, policymakers, and researchers to gauge public sentiment in real-time. These systems utilize advanced natural language processing (NLP) models, including Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformer (GPT), and Text-To-Text Transfer Transformer (T5), alongside traditional machine learning algorithms. Deep learning enhances the ability to model complex relationships within data, enabling more accurate sentiment classification. BERT excels in understanding bidirectional context, GPT leverages pre-training to generate contextually accurate language models, and T5 reframes various NLP tasks as a text-to-text problem, enhancing versatility in sentiment analysis. By identifying sentiment polarity—positive, negative, or neutral—across various platforms, these technologies allow organizations to better understand customer feedback, enhance marketing strategies, and monitor brand reputation. Additionally, AI sentiment analysis enables early detection of emerging issues, helping to address potential crises proactively. As the demand for real-time sentiment analysis grows, advancements in AI continue to refine accuracy, handling diverse linguistic nuances, slang, and contextual challenges inherent to social media communication.},
        keywords = {Sentiment Analysis, Artificial Intelligence, Deep Learning, Social Media, Natural Language Processing, BERT, GPT, T5, Machine Learning.},
        month = {November},
        }