Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment

We describe a classifier for predicting message-level sentiment of English microblog messages from Twitter. This paper describes our submission to the SemEval2015 competition (Task 10). Our approach is to combine several variants of our previous year’s SVM system into one meta-classifier, which was then trained using a random forest. The main idea is that the meta-classifier allows the combination of the strengths and overcome some of the weaknesses of the artificially-built individual classifiers, and adds additional non-linearity. We were also able to improve the linear classifiers by using a new regularization technique we call flipout.