News arrive randomly and the resulting sentiment, after quantification, behaves like a stochastic process. We propose a sentiment-driven stochastic volatility model to characterize the evolution of sentiment and its interplay with price and volatility process. Specifically, we propose a mean-reverting (Ornstein-Uhlenbeck) sentiment stochastic process to push over-optimistic/pessmistic sentiment back to neutral mood. Using the high-frequency news from Nasdaq news platform, we quantify news and model sentiment in the continuous time fashion. We find the presence of sentiment defers the speed of volatility reversion, and the simulated price through the sentiment-driven stochastic volatility model better approach the actual evolution of S&P 500 index.