LLT-PolyU: Identifying Sentiment Intensity in Ironic Tweets
Hongzhi Xu, Enrico Santus, Anna Xenia Laszlo, Chu‐Ren Huang · 2015
In this paper, we describe the system we built for Task 11 of SemEval2015, which aims at i-dentifying the sentiment intensity of figurative language in tweets. We use various features, including those specially concerned with the identification of irony and sarcasm. The fea-tures are evaluated through a decision tree re-gression model and a support vector regres-sion model. The experiment result of the five-cross validation on the training data shows that the tree regression model outperforms the sup-port vector regression model. The former is therefore used for the final evaluation of the task. The results show that our model per-forms especially well in predicting the senti-ment intensity of tweets involving irony and sarcasm. 1