This article explores the relationship between public mood and socio-economic phenomena by analyzing Twitter sentiment data from August 1 to December 20, 2008. The authors use an extended version of the Profile of Mood States (POMS) to extract six dimensions of mood (tension, depression, anger, vigor, fatigue, confusion) from a large corpus of tweets. They compare these mood trends to fluctuations in stock market and crude oil price indices, as well as major events such as the U.S. Presidential Election and Thanksgiving Day. The results show that social, political, cultural, and economic events have a significant, immediate, and specific effect on public mood, with short-term events like elections and holidays having a more pronounced impact than long-term economic changes. The study also highlights the potential of using large-scale sentiment analysis to model collective emotive trends and their predictive value for social and economic indicators.This article explores the relationship between public mood and socio-economic phenomena by analyzing Twitter sentiment data from August 1 to December 20, 2008. The authors use an extended version of the Profile of Mood States (POMS) to extract six dimensions of mood (tension, depression, anger, vigor, fatigue, confusion) from a large corpus of tweets. They compare these mood trends to fluctuations in stock market and crude oil price indices, as well as major events such as the U.S. Presidential Election and Thanksgiving Day. The results show that social, political, cultural, and economic events have a significant, immediate, and specific effect on public mood, with short-term events like elections and holidays having a more pronounced impact than long-term economic changes. The study also highlights the potential of using large-scale sentiment analysis to model collective emotive trends and their predictive value for social and economic indicators.