This study investigates the intersection of digital behavior and financial market activity by employing Google Trends data as a non-traditional, data driven indicator of investor sentiment in the Indian context. Recognizing that investor psychology often drives market prices beyond fundamental values, the research explores whether web search query data can effectively capture and reflect investor sentiment and its influence on Nifty 50 index returns. Using the Google Sentiment Index (GSI) as a proxy for investor mood, the study employs quantile regression and Vector Autoregression (VAR) models to analyze the dynamic relationship between sentiment and market performance across varying return distributions. The quantile regression results indicate that the relationship between GSI and Nifty returns is asymmetric and non-linear with a significant negative effect during bearish conditions and a positive effect during bullish phases. This pattern highlights that sentiment exerts stronger short-term influence in extreme market conditions. The VAR analysis indicates a bidirectional feedback relationship between sentiment and returns; however, the predictive power of returns on sentiment is more pronounced, suggesting that investor sentiment is largely reactive to past market performance rather than predictive of future movements. Variance decomposition further confirms that daily market fluctuations are primarily self-driven, with sentiment playing a minimal role in explaining short-term return variance. The findings underscore that while sentiment derived from online behavior offers valuable behavioral insights, it serves as a weak predictor of daily returns and is more useful for identifying broader market trends and risk dynamics. By integrating behavioral finance with big data analytics, this research demonstrates the potential of Google search activity as a real-time tool for monitoring investor psychology, enhancing risk management, and informing strategic investment decisions in the Indian financial markets.