The study proposes an alternative way to decompose Federal Reserve (Fed) information shocks from monetary policy shocks by employing a textual analysis to Federal Open Market Committee (FOMC) statements. I decompose Fed statements into economic topics using Latent Dirichlet Allocation (LDA). The model was trained on the business section from major US newspapers. After decomposing surprises in Fed futures into a part that is explained by topics from the Fed statements and that is not explained, the study employs these purged series as proxies for monetary policy and Fed information shocks. The results show that, compared to surprises in 3-month federal funds futures, a policy shock identified in this study has a more negative effect on GDP and a more prolonged negative effect on inflation. In the short-run it causes S&P500 to decline and the Fed to raise its interest rate. Identified Fed information shock affects the macroeconomy as the standard news shock: it has positive long-run effects on S&P500, interest rates, and real GDP, whereas it has a negative short-run effect on inflation. Moreover, the Fed information shock reduces credit costs.