LLT-PolyU: Identifying Sentiment Intensity in Ironic Tweets

Abstract

In this paper, we describe the system we built for Task 11 of SemEval2015, which aims at identifying the sentiment intensity of figurative language in tweets. We use various features, including those specially concerned with the identification of irony and sarcasm. The features are evaluated through a decision tree regression model and a support vector regression model. The experiment result of the five-cross validation on the training data shows that the tree regression model outperforms the support vector regression model. The former is therefore used for the final evaluation of the task. The results show that our model performs especially well in predicting the sentiment intensity of tweets involving irony and sarcasm.