Around the world, several countries have adopted in ation targeting as their monetary policy framework. These institutions set their target interest rates in monetary policy meetings. These decisions are then circulated through press releases that explain the policy rationale. The information contained in the press releases includes current policies, economic outlook, and signals about likely future policies. In this paper, using linguistic methods, such as Latent Dirichlet Allocation (LDA) and semi-automated content analysis, we examine the information contained in the monetary press releases of in ation targeting countries. In addition, we build a custom dictionary for analyzing monetary policy press releases. Using Semi-automated Content Analysis, we then develop a measure, which we refer to as the Sentiment Score index, that quanties the policy tilt implied in the information provided in the press releases. We nd that for a signicant majority of the in ation targeting countries, the index provides additional information that helps predict monetary policy rate movements.