Abstract: A method of investigating public mood from a multi-dimensional model approach and a method to predict economic market trends above chance level based on the multi-dimensional model approach are provided. The text-content of several large-scale collections of daily network communications are analyzed via mood assessment tools, measuring various mood dimensions. A Granger Causality analysis investigated the correlation between daily changes in public mood states via results of the daily mood time series of the mood assessment tools with changes in value of the Dow Jones Industrial Average (“DJIA”) over time. Based on the above investigation, a Self-Organizing Fuzzy Neural Network model was trained to predict next-day DJIA value based on a combination of past DJIA values and public mood state measurements across several specified mood dimensions, such as calm and a combination of calm and happy.