Lsislif: Feature Extraction and Label Weighting for Sentiment Analysis in Twitter

Abstract

This paper describes our sentiment analysis systems which have been built for SemEval-2015 Task 10 Subtask B and E. For sub-task B, a Logistic Regression classifier has been trained after extracting several groups of features including lexical, syntactic, lexicon-based, Z score and semantic features. A weighting schema has been adapted for pos-itive and negative labels in order to take into account the unbalanced distribution of tweets between the positive and negative classes. This system is ranked third over 40 partici-pants, it achieves average F1 64.27 on Twit-ter data set 2015 just 0.57 % less than the first system. We also present our participation in Subtask E in which our system has got the sec-ond rank with Kendall metric but the first one with Spearman for ranking twitter terms ac-cording to their association with the positive sentiment. 1