In this project, a Bayesian algorithm is used to classify sentiment of global tweets with possible applications in algorithmic trading. We demonstrate that we can perform semi-automatic training of the classifier by using messages that contain sentimental features such as emoticons. Classified messages are fed back into the engine, and we show a rudimentary measurement of classification accuracy.

See the presentation slides.