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@article{188036,
        author = {Vishwanathan J R},
        title = {Rethinking Market Signals: The Role of NLP-Driven Sentiment Analytics in Modern Financial Forecasting and Risk Assessment},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {780-784},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188036},
        abstract = {Natural Language Processing has become a critical component of modern financial analysis as markets increasingly respond to the tone, intent, and sentiment expressed across news, earnings calls, social media, and regulatory disclosures. By transforming unstructured text into measurable signals, advanced NLP models, ranging from lexicon-based systems to transformer architectures like FinBERT, offer richer insights into market expectations, investor behaviour, and early indicators of volatility. This review examines how sentiment intelligence enhances forecasting accuracy, strengthens risk interpretation, and supports data-driven portfolio decisions when integrated with traditional financial metrics. It also highlights emerging gaps, including the need for dynamic sentiment–volatility frameworks, explainable NLP methods for investment decisions, and domain-specific lexicons for non-Western markets, positioning NLP as a foundational analytical layer in future financial ecosystems.},
        keywords = {},
        month = {December},
        }