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@article{176165,
        author = {ADITHYA MENON and MUHAMMED SHIBIL C P and Dr.N.Saranya, Assistant Professor and Head and Vineetha Vijayan, Assistant Professor},
        title = {Enhancing Stock Market Forecasting with BERT and Facebook Prophet: A Multi-Faceted AI Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5879-5884},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176165},
        abstract = {Stock market forecasting remains a challenging task due to the volatile and dynamic nature of financial markets. Traditional methods often fail to capture complex patterns in financial data, leading to suboptimal predictions. This paper presents a novel AI-driven approach combining Bidirectional Encoder Representations from Transformers (BERT) for sentiment analysis and Facebook Prophet for time-series forecasting. By integrating sentiment-driven insights with robust time-series modeling, we enhance predictive accuracy and provide a more holistic market analysis. Our methodology involves collecting financial news and social media data, fine-tuning BERT for sentiment classification, and incorporating sentiment scores into Prophet’s predictive framework. Experimental results on real-world stock data demonstrate that our hybrid model outperforms standalone forecasting models in terms of accuracy and robustness. The findings underscore the potential of AI-driven sentiment-aware forecasting techniques in financial analytics, aiding investors and analysts in making more informed decisions.},
        keywords = {Market Forecasting, BERT, Facebook Prophet, Sentiment Analysis, Time-Series Forecasting, Artificial Intelligence.},
        month = {April},
        }