Of late there has been a significant amount of work on using sources of text data from the Web (such as Twitter or Google Trends) to predict financial and economic variables of interest. Much of this work has relied on some form or other of superficial sentiment analysis to represent the text. In this work we present a novel approach to predicting economic variables using sentiment composition over text streams of Web data. We treat each text stream as a separate sentiment source with its own predictive distribution. We then use a Bayesian classifier combination model to combine the separate predictions into a single optimal prediction for the Nonfarm Payroll index, a primary economic indicator. Our results show that we can achieve high predictive accuracy using sentiment over big text streams.