EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets
This work addresses emotion detection in social media for Arabic language processing, representing an incremental improvement in a specific domain.
The authors tackled emotion analysis in tweets by combining N-Stream ConvNets and XGBoost regressors, achieving top performance in Arabic valence intensity regression and ordinal classification tasks at SemEval-2018.
This paper describes our system that has been used in Task1 Affect in Tweets. We combine two different approaches. The first one called N-Stream ConvNets, which is a deep learning approach where the second one is XGboost regresseor based on a set of embedding and lexicons based features. Our system was evaluated on the testing sets of the tasks outperforming all other approaches for the Arabic version of valence intensity regression task and valence ordinal classification task.