摘要
Forecasting economic indices on the basis of information extracted from text documents, like newspaper articles is an attractive idea. With the help of text mining techniques, in particular sentiment analysis, we evaluate the tone of individual New York Times （NYT） articles and compare our results to the Chicago Fed National Activity Index （CFNAI）. In this paper, we present a simple, intuitive framework to derive sentiment scores from text documents In particular articles are tagged based on terms and their connotated sentiment. Subsequently, we forecast the CFNAI movements via support vector machines （SVM） trained on a subset of the observed sentiment scores. We apply our model into two different data sets, the whole NYT articles and the articles categorized as NYT business news. On both data sets, we applied a simple performance measure to evaluate forecasting accuracy of the CFNAI
