| Cyberbullying detection is a global issue that must be addressed to improve the cyberspace for millions of online users, services, and organizations. Online harassment of the general public and celebrities is now commonplace on social media, particularly in Bangladesh. In this paper, we present a novel multi-feature transformer followed by a deep neural network for multiple-dimensional cyberbullying detection. Using online Bangla textual data, we introduce the user's social profile, the lexical features, the contextual embedding, and the semantic similarities among word associations in Bangla in order to develop an effective and robust cyberbullying detection system. Our proposed method can detect cyberbullying in Bangla with a 98% detection accuracy for threats and a 90% detection accuracy for sarcastic comments. The aggregate accuracy of all six multiclass labels is 86.3%. In addition, the experimental results find that the proposed technique outperforms the state-of-the-art methods for detecting cyberbully in Bangla. |
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