I investigate the role that news sentiment plays in the macroeconomy. Using an approach that combines Doc2Vec embedding and Latent Dirichlet Allocation with lexical-based models I show that the news the media choose to report and the tone of these reports contain important information for household unemployment, interest rates, and inflation expectations. Topic time series derived from the news and the sentiments they express are employed to estimate how the news affects the macroeconomy.
