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

Abstract

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.

A Twitter Corpus and Benchmark Resources for German Sentiment Analysis

“…For comparison, we implemented a featurebased system using a Support Vector Machine (SVM). Feature selection is based on the system described in (Uzdilli et al, 2015), which ranked 8th in the Semeval competition of 2015, and include n-gram, various lexical features, and statistical text properties. We use the macro-averages F1-score of positive and negative class, i.e.…”

“…We now study how the CNN performs when trained and/or tested on the three German sentiment corpora we are aware of: SB10k (from this paper, 9738 tweets), MGS corpus (109'130 tweets, (Mozetič et al, 2016)), and DAI corpus (1800 tweets, (Narr et al, 2012) For comparison, we implemented a featurebased system using a Support Vector Machine (SVM). Feature selection is based on the system described in (Uzdilli et al, 2015), which ranked 8th in the Semeval competition of 2015, and include n-gram, various lexical features, and statistical text properties. We use the macro-averages F1-score of positive and negative class, i.e.…”