Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment
Fatih Uzdilli, Martin Jaggi, Dominic Egger, Pascal Julmy, Leon Derczynski, Mark Cieliebak · 2015
We describe a classifier for predicting message-level sentiment of English microblog messages from Twitter.This paper describes our submission to the SemEval-2015 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.