Transfer Learning model for Social Emotion Prediction using Writers Emotions in Comments

Abstract

Social emotion prediction is concerned with the prediction of the reader’s emotion when exposed to a text. In this paper, we propose a transfer learning approach to social emotion prediction, where the source task is writer’s emotion prediction, an area in which models are advanced due to the rich literature and availability of large and high-quality training datasets. We utilized a pre-trained writer’s emotion prediction model to predict the writer’s emotion in comments, then we aggregated the emotions and trained a classifier to predict social emotion for posts. Results show that pre-trained models for writer’s emotion prediction can improve the prediction of social emotion. Furthermore, we demonstrate that our proposed model outperforms popular models in terms of F1-score and performs similarly to the best model in terms of Acc@1.