senti.ue-en: an approach for informally written short texts in semeval-2013 sentiment analysis task

Abstract

This article describes a Sentiment Analysis (SA) system named senti.ue-en, built for participation in SemEval-2013 Task 2, a Twitter SA challenge. In both challenge subtasks we used the same supervised machine learning approach, including two classifiers in pipeline, with 22 semantic oriented features, such as polarized term presence and index, and negation presence. Our system achieved a better score on Task A (0.7413) than in the Task B (0.4785). In the first subtask, there is a better result for SMS than the obtained for the more trained type of data, the tweets.