ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models

Abstract

In this paper we describe our systems submitted to Semeval 2018 Task 1 “Affect in Tweet” (Mohammad et al., 2018). We participated in all subtasks of English tweets, including emotion intensity classification and quantification, valence intensity classification and quantification. In our systems, we extracted four types of features, including linguistic, sentiment lexicon, emotion lexicon and domain-specific features, then fed them to different regressors, finally combined the models to create an ensemble for the better performance. Officially released results showed that our system can be further extended.