• Bharath N
• Aaryan C R
• Mohammed Farhan A
• Nandish Kumar S
• Keerthi Mohan

The Indian stock market is characterized by high volatility, rapid information flows, and diverse investor behavior, making accurate prediction and risk management a complex challenge. Traditional analytics that rely solely on historical price data often fail to capture the nuanced impact of market sentiment, especially as it emerges from news, social media, and financial disclosures. This survey reviews and categorizes existing approaches to sentiment-enhanced financial analytics for Indian markets, focusing on multi-source sentiment extraction, hybrid predictive modelling, and explainable AI (XAI) for risk management. We compare models such as FinBERT, XLNet, ARIMA, LSTM, and reinforcement learning, highlighting their strengths, limitations, and application to Indian financial data. The paper also identifies research gaps and proposes future directions for building more robust, transparent, and context-aware financial analytics platforms.

Financial Analytics, Sentiment Analysis, Explainable AI, Risk Management, Indian Stock Market, Hybrid Modelling, Deep Learning, NLP.

"Study on Advanced AI Models for Sentiment-Enhanced Financial Analytics and Risk Prediction in India", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.a239-a241, June-2025, Available :http://www.jetir.org/papers/JETIR2506030.pdf

"Study on Advanced AI Models for Sentiment-Enhanced Financial Analytics and Risk Prediction in India", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. ppa239-a241, June-2025, Available at : http://www.jetir.org/papers/JETIR2506030.pdf