A Context Integrated Model for Multi-label Emotion Detection

This paper explores the impact of taking the environment within which a tweet is made, on the task of analyzing sentiment orientations of tweets produced by people in the same community. The paper proposes C-GRU (Context-aware Gated Recurrent Units), which extracts the contextual information (topics) from tweets and uses them as an extra layer to determine sentiments conveyed by the tweet. The proposed architecture learns direct co-relations between such information and the task’s predication. The multi-modal model combines both outputs learnt (from topics and sentences) by learning the contribution weights of the two sub-modals to the predictions. The evaluation of the proposed model which is carried out by applying it to the SemEval-2018 dataset for Arabic multi-label emotion classification, demonstrate that the model outperforms the highest reported performer on the same dataset.