Cyberbullying Detection Based on Emotion

Due to the detrimental consequences caused by cyberbullying, a great deal of research has been undertaken to propose effective techniques to resolve this reoccurring problem. The research presented in this paper is motivated by the fact that negative emotions can be caused by cyberbullying. This paper proposes cyberbullying detection models that are trained based on contextual, emotions and sentiment features. An Emotion Detection Model (EDM) was constructed using Twitter datasets that have been improved in terms of its annotations. Emotions and sentiment were extracted from cyberbullying datasets using EDM and lexicons based. Two cyberbullying datasets from Wikipedia and Twitter respectively were further improved by comprehensive annotation of emotion and sentiment features. The results show that anger, fear and guilt were the major emotions associated with cyberbullying. Subsequently, the extracted emotions were used as features in addition to contextual and sentiment features to train models for cyberbullying detection. The results demonstrate that using emotion features and sentiment has improved the performance of detecting cyberbullying by 0.5 to 0.6 recall. The proposed models also outperformed the state-of-the-art models by a 0.7 f1-score. The main contribution of this work is two-fold, which includes a comprehensive emotion-annotated dataset for cyberbullying detection, and an empirical proof of emotions as effective features for cyberbullying detection.

Automated summary

In order to address the negative impact of cyberbullying, extensive research has been conducted to propose effective solutions. This paper introduces cyberbullying detection models that utilize contextual, emotions, and sentiment features. An Emotion Detection Model (EDM) is developed using improved Twitter datasets with annotated emotions. Emotions and sentiment are extracted from cyberbullying datasets using the EDM and lexicons. The results reveal anger, fear, and guilt as major emotions associated with cyberbullying. The proposed models, incorporating emotion features and sentiment, outperform existing models in terms of recall by 0.5 to 0.6 and f1-score by 0.7. The contributions of this work include a comprehensive emotion-annotated dataset for cyberbullying detection and empirical evidence of emotions as effective features in cyberbullying detection.