Paper: GPLSI: Supervised Sentiment Analysis in Twitter using Skipgrams

ACL ID S14-2048
Title GPLSI: Supervised Sentiment Analysis in Twitter using Skipgrams
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

In this paper we describe the system sub- mitted for the SemEval 2014 Task 9 (Sen- timent Analysis in Twitter) Subtask B. Our contribution consists of a supervised ap- proach using machine learning techniques, which uses the terms in the dataset as fea- tures. In this work we do not employ any external knowledge and resources. The novelty of our approach lies in the use of words, ngrams and skipgrams (not- adjacent ngrams) as features, and how they are weighted.