Consumer sentiment, which measures how confident individuals feel in the strength of the economy, is a crucial indicator of the overall health of the economy. However, due to the time and costs associated with collecting the survey responses associated with the Index of Consumer Sentiment (ICS), along with the delayed nature of releasing this information, there is motivation to find alternative data sources to the ICS. In this project, we investigated utilizing news headlines as an alternative signal to gauge consumer sentiment in the United States. More specifically, we utilized natural language processing techniques such as latent Dirichlet allocation (LDA) and sentiment analysis to extract quantifiable topics and sentiments from news headlines on the front page of top publications' websites. We subsequently used that information as predictors for the monthly personal saving and labor force participation rates. The topics and sentiments served as exogenous inputs in a Seasonal Autoregressive Integrated Moving Average with eXogenous regressors (SARIMAX) model to predict the actual rates, and as covariates in classification models to predict the direction of rate movement. Our findings showed that topic-sentiment combinations from news headlines have considerable predictive power in modeling future economic conditions even when comparing to the predictive power of the ICS.