Sentiment and topic analysis of news articles and central bank communications is highly valuable for understanding macroeconomics trends. The sentiment captured from these texts can supplement traditional economic indicators, as central bank communications influence consumer expectations while news capture market sentiment. Traditional sentiment analysis approaches for economic documents rely mainly on lexicon-based methods, which suffer from context-blindness, limited flexibility and language dependency. Large Language Models (LLMs) represent a promising alternative, combining deeper contextual understanding and the ability to learn from few examples without requiring expensive fine-tuning. This thesis explores the use of LLMs to automatically annotate sentiment and topic of Italian news articles and European Central Bank (ECB) bulletins.