As the amount of information available on the internet has increased sharply in the last years, methods for measuring and comparing text-based information is gaining popularity on financial markets. Text mining and natural language processing has become an important tool for classifying large collections of texts or documents. One field of applications is topic modelling of the minutes from central banks' monetary policy meetings, which tend to be about topics such as"inflation", "economic growth" and "rates". The central bank of Sweden is the Riksbank, which hold 6 annual monetary policy meetings where the members of the Executive Board decide on the new repo rate. Two weeks later, the minutes of the meeting is published and information regarding the future monetary policy is given to the market in the form of text. This information has before release been unknown to the market, thus having the potential to be market-sensitive. Using Latent Dirichlet Allocation (LDA), an algorithm used for uncovering latent topics in documents, the topics in the meeting minutes should be possible to identify and quantify. In this project, 8 topics were found regarding, among other, inflation, rates, household debt and economic development. An important factor in analysis of central bank communication is the underlying tone in the discussions. It is common to classify central bankers as hawkish or dovish. Hawkish members of the board tend to favour tightening monetary policy and rate hikes, while more dovish members advocate a more expansive monetary policy and rate cuts. Thus, analysing the tone of the minutes can give an indication of future moves of the monetary policy rate. The purpose of this project is to provide a fast method for analysing the minutes from the Riksbank monetary policy meetings. The project is divided into two parts. First, a LDA model was trained to identify the topics in the minutes, which was then used to compare the content of two consecutive meeting minutes. Next, the sentiment was measured as a degree of hawkishness or dovishness. This was done by categorising each sentence in terms of their content, and then counting words with hawkish or dovish sentiment. The resulting net score gives larger values to more hawkish minutes and was shown to follow the repo rate path well. At the time of the release of the minutes, the new repo rate is already known, but the net score does gives an indication of the stance of the board.