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@article{205118,
        author = {Ayush Pramod Bhalwankar and Parth Patil and Satyajeet Kachole and Harshal Shinde},
        title = {Real time stock market prediction with Transformer Models and News Sentiment Integration},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {5906-5909},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205118},
        abstract = {This research proposes a hybrid framework for stock market trend prediction by integrating news sentiment analysis, technical indicators, and fundamental financial metrics. Unlike traditional models that rely on isolated historical or quantitative data, the approach applies advanced NLP techniques such as sentiment analysis, named entity recognition, and topic modeling to extract insights from financial news. These qualitative features are combined with technical indicators and fundamental ratios to build a comprehensive prediction system. Machine learning and deep learning models, including SVM, Random Forest, LSTM, and BERT, are trained on stock prices, financial reports, and curated news datasets. Results show improved accuracy, responsiveness, and robustness, especially during volatile market conditions, making the model valuable for investors and analysts.},
        keywords = {},
        month = {June},
        }