Prerna Khurana


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Detecting Aggression and Toxicity using a Multi Dimension Capsule Network
Saurabh Srivastava | Prerna Khurana
Proceedings of the Third Workshop on Abusive Language Online

In the era of social media, hate speech, trolling and verbal abuse have become a common issue. We present an approach to automatically classify such statements, using a new deep learning architecture. Our model comprises of a Multi Dimension Capsule Network that generates the representation of sentences which we use for classification. We further provide an analysis of our model’s interpretation of such statements. We compare the results of our model with state-of-art classification algorithms and demonstrate our model’s ability. It also has the capability to handle comments that are written in both Hindi and English, which are provided in the TRAC dataset. We also compare results on Kaggle’s Toxic comment classification dataset.


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Identifying Aggression and Toxicity in Comments using Capsule Network
Saurabh Srivastava | Prerna Khurana | Vartika Tewari
Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)

Aggression and related activities like trolling, hate speech etc. involve toxic comments in various forms. These are common scenarios in today’s time and websites react by shutting down their comment sections. To tackle this, an algorithmic solution is preferred to human moderation which is slow and expensive. In this paper, we propose a single model capsule network with focal loss to achieve this task which is suitable for production environment. Our model achieves competitive results over other strong baseline methods, which show its effectiveness and that focal loss exhibits significant improvement in such cases where class imbalance is a regular issue. Additionally, we show that the problem of extensive data preprocessing, data augmentation can be tackled by capsule networks implicitly. We achieve an overall ROC AUC of 98.46 on Kaggle-toxic comment dataset and show that it beats other architectures by a good margin. As comments tend to be written in more than one language, and transliteration is a common problem, we further show that our model handles this effectively by applying our model on TRAC shared task dataset which contains comments in code-mixed Hindi-English.