Abstract:
The usage of social networking platforms has accelerated rapidly with technological
advancement in the last few decades. Facebook, Twitter, and YouTube are extensively
utilized platforms for social communication, interaction, marketing, and information
sharing. So abusive text and comments are widespread on those platforms, negatively
impacting the expected user experience. Besides, Bangla is the seventh most widely
spoken language worldwide, and a significant number of users use Bangla on those
platforms. In this study, I have concentrated on detecting abusive Bangla comments in
discussion threads from social platforms, i.e., Facebook and YouTube. After extensive
pre-processing, tokenization, sequencing and padding Bidirectional Long Short-Term
Memory (BiLSTM) with attention layer is trained utilizing the dataset consisting of a
total of 12,022 comments which are labeled into two categories, abusive and nonabusive.
In this study, I have estimated the accuracy, precision, recall, and f1-score to
assess our models. Our proposed model performed notably with an accuracy of 94%.
