This paper investigates whether public mood, as measured from large-scale Twitter feeds, can predict changes in the Dow Jones Industrial Average (DJIA). The authors use two tools—OpinionFinder and Google-Profile of Mood States (GPOMS)—to analyze the text content of daily tweets to measure positive vs. negative mood and six dimensions of mood (Calm, Alert, Sure, Vital, Kind, and Happy), respectively. They validate the tools' ability to capture public mood responses to significant socio-cultural events like the U.S. presidential election and Thanksgiving. The results show that certain mood dimensions, particularly Calm and Happy, are predictive of DJIA changes. A Self-Organizing Fuzzy Neural Network (SOFNN) model trained on past DJIA values and public mood data demonstrates significant improvements in predicting DJIA closing values, achieving an accuracy of 87.6% in predicting daily up and down changes. The study highlights the potential of using Twitter data to predict stock market movements and suggests that public mood analysis can provide valuable insights into economic indicators.This paper investigates whether public mood, as measured from large-scale Twitter feeds, can predict changes in the Dow Jones Industrial Average (DJIA). The authors use two tools—OpinionFinder and Google-Profile of Mood States (GPOMS)—to analyze the text content of daily tweets to measure positive vs. negative mood and six dimensions of mood (Calm, Alert, Sure, Vital, Kind, and Happy), respectively. They validate the tools' ability to capture public mood responses to significant socio-cultural events like the U.S. presidential election and Thanksgiving. The results show that certain mood dimensions, particularly Calm and Happy, are predictive of DJIA changes. A Self-Organizing Fuzzy Neural Network (SOFNN) model trained on past DJIA values and public mood data demonstrates significant improvements in predicting DJIA closing values, achieving an accuracy of 87.6% in predicting daily up and down changes. The study highlights the potential of using Twitter data to predict stock market movements and suggests that public mood analysis can provide valuable insights into economic indicators.