Abstract
This paper presents a novel application in the emerging field of computational politics. Here, we anticipate American congressional voting outcome using support vector machines (SVM) and back propagation learning algorithm neural network models. Our proposed method successfully associates opinions of American congress members on defined national issues with their political party affiliation as republican or democrat, thus providing a artificially intelligent anticipation system of the congressional voting outcome based on previous knowledge of how the congress members perceive national issues. Knowledge is obtained from existing congressional records which are unclassified and available online for research purposes. The obtained experimental results suggest that our novel method and application can be further applied to similar voting polls in order to anticipate the party members voting inclination.
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