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@article{169419,
        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 = {1249-1255},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169419},
        abstract = {Social media platforms have become a significant source of public opinion, providing vast amounts of unstructured textual data that reflects people's thoughts, attitudes, and emotions. Sentiment analysis, the process of identifying and categorizing sentiments expressed in this data, has evolved with the rise of artificial intelligence (AI).
This paper explores AI-powered sentiment analysis, highlighting the role of machine learning and natural language processing (NLP) in enhancing the accuracy and efficiency of analyzing social media data. The study reviews traditional sentiment analysis methods and contrasts them with modern AI approaches such as deep learning models and transformers like BERT and GPT.
It also discusses challenges in AI-driven sentiment analysis, such as sarcasm detection, multilingual text processing, and contextual understanding, while outlining key applications across industries, including marketing, politics, finance, and public health. The research concludes by examining the future potential of AI technologies in refining sentiment analysis for more accurate real-time insights.
The research emphasizes the potential of AI to further enhance the capability of sentiment analysis, making it a valuable tool for interpreting social media data in real time and on a large scale.},
        keywords = {AI, sentiment analysis, social media, machine learning, natural language processing, deep learning, BERT, GPT, public opinion, real-time analysis.},
        month = {November},
        }