A Deep Learning Framework for Assamese Toxic Comment Detection: Leveraging LSTM and BiLSTM Models with Attention Mechanism

As social media platforms grow in popularity, this research piece discusses the significance of creating a secure and positive online environment. The major goal is to protect users by detecting objectionable language in Assamese social media comments. The ultimate goal is to create a very effective mechanism for detecting toxic comments in Assamese, supporting a safe online environment. To address the lack of available datasets, a well-curated dataset was manually assembled for the experiment. Deep learning models such as LSTM and bidirectional LSTM (BiLSTM) were used to capture the contextual intricacies of user-generated comments. Notably, the BiLSTM model beats the LSTM model by including an attention mechanism, attaining a promising accuracy rate of 86.9% in successfully identifying toxic comments. Using the capabilities of the LSTM and BiLSTM models, a more robust and efficient approach for recognizing toxic phrases in Assamese is developed, aligned with the goal of building a secure, respectful, and toxic-free online environment.