The influence of daily news on the economy, especially stock market, cannot be underestimated with big data gaining its momentum. In this article, the author uses two years of daily news headlines to predict the stock market movements of the Dow Jones Industrial Average. The first treatments on the dataset are collecting news from Kaggle.com and stock data from Yahoo Finance. The two datasets are then combined into one CSV file and split into training and test sets. Two machine learning models, Logistic Regression and Long Short-Term Memory, are built to fit the combined dataset, and the test index is the accuracy of prediction. The test accuracy is 0.58 with the three-word phrase by Logistic Regression and 0.65 after ten times training with the LSTM model. The final result demonstrates that the two models are feasible and effective for seeking the relationship between daily news and stock market movements and, thus, valuable for stock prediction. The attempts to set parameters give reference to further study, especially the word count of phrases and the number of training circulation.