Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-Grams

Abstract

We describe a classifier to predict the message-level sentiment of English micro-blog messages from Twitter. This paper describes the classifier submitted to the SemEval-2014 competition (Task 9B). Our approach was to build up on the system of the last year’s winning approach by NRC Canada 2013 (Mohammad et al., 2013), with some modifications and additions of features, and additional sentiment lexicons. Furthermore, we used a sparse (`1-regularized) SVM, instead of the more commonly used `2-regularization, resulting in a very sparse linear classifier.