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

ACL ID S14-2105
Title Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-Grams
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

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.