  We propose how to quantify high-frequency market sentiment using
high-frequency news from NASDAQ news platform and support vector machine
classifiers. News arrive at markets randomly and the resulting news sentiment
behaves like a stochastic process. To characterize the joint evolution of
sentiment, price, and volatility, we introduce a unified continuous-time
sentiment-driven stochastic volatility model. We provide closed-form formulas
for moments of the volatility and news sentiment processes and study the news
impact. Further, we implement a simulation-based method to calibrate the
parameters. Empirically, we document that news sentiment raises the threshold
of volatility reversion, sustaining high market volatility.
