Cyberbullying Detection Using Deep Neural Network from Social Media Comments in Bangla Language
This addresses the problem of online harassment for Bengali speakers, but it is incremental as it applies existing deep learning methods to a new language dataset.
The paper tackled cyberbullying detection in Bengali social media comments by proposing binary and multiclass classification models using a hybrid neural network, achieving 87.91% accuracy for binary classification and 85% for multiclass classification.
Cyberbullying or Online harassment detection on social media for various major languages is currently being given a good amount of focus by researchers worldwide. Being the seventh most speaking language in the world and increasing usage of online platform among the Bengali speaking people urge to find effective detection technique to handle the online harassment. In this paper, we have proposed binary and multiclass classification model using hybrid neural network for bully expression detection in Bengali language. We have used 44,001 users comments from popular public Facebook pages, which fall into five classes - Non-bully, Sexual, Threat, Troll and Religious. We have examined the performance of our proposed models from different perspective. Our binary classification model gives 87.91% accuracy, whereas introducing ensemble technique after neural network for multiclass classification, we got 85% accuracy.