Crude oil futures are worlds the most actively traded commodity futures, with more than 3 billion barrels per year in open interest. In this thesis we use related news to model the price dynamics of oil futures. We examine the empirical patterns of oil market news data processed by Thompson Reuters News Analytics, plus the intraday trading data of the WTI futures price traded on NYMEX. Then we build a three factor stochastic model for futures prices on the whole curve, using interest rate, convenience yield and spot price. The Kalman filter was used to obtain quasi-maximum likelihood estimators. We found that news can significantly explain the price movements and volatility clustering, as well as its skewness and kurtosis. We also found that negative news has an higher explanatory power of price dynamics than positive news, indicating an asymmetrical behavior of information with different tones.