ECNU at SemEval-2016 task 7: An enhanced supervised learning method for Lexicon Sentiment Intensity ranking

Abstract

This paper describes our system submissions to task 7 in SemEval 2016, i.e., Determining Sentiment Intensity. We participated the first two subtasks in English, which are to predict the sentiment intensity of a word or a phrase in English Twitter and General English domains. To address this task, we present a supervised learning-to-rank system to predict the relevant scores, i.e., the strength associated with positive sentiment, for English words or phrases. Multiple linguistic and sentiment features are adopted, e.g., Sentiment Lexicons, Sentiment Word Vectors, Word Vectors, Linguistic Features, etc. Officially released results showed that our systems rank the 1st among all submissions in English, which proves the effectiveness of the proposed method.