Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-Grams
Martin Jaggi, Fatih Uzdilli, Mark Cieliebak · 2014
We describe a classifier to predict the message-level sentiment of English micro-blog messages from Twitter. This pa-per describes the classifier submitted to the SemEval-2014 competition (Task 9B). Our approach was to build up on the sys-tem of the last year’s winning approach by NRC Canada 2013 (Mohammad et al., 2013), with some modifications and addi-tions of features, and additional sentiment lexicons. Furthermore, we used a sparse (`1-regularized) SVM, instead of the more commonly used `2-regularization, result-ing in a very sparse linear classifier. 1