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

摘要

We describe a classifier for predicting message-level sentiment of English micro-blog 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.