A Multi-Task LSTM Approach for Comprehensive Detection of Emotion, Hate Speech, and Violence in Text

Increase in the frequency of availability of user-generated content on digital platforms is hampering the detection of harmful and sensitive language. This paper it presents an approach by which we can address this challenge by employing a multi-task model based on LSTM network, a type of RNN particularly designed to process and retain the information over multiple time steps. The multi-task NLP model is designed to classify three categories: emotions, hate speech and violence. Utilizing the LTSM networks we train the model on distinct text-based dataset for each task. This approach will process the different dataset for each task separately through a shared architecture. This model will employ shared layers, including LSTM, Pooling, Embedding and Dropout. Multi-task framework is designed to capture the common linguistic features between task by sharing the core layers while maintaining the task specified output layer for the independent predictions. This will enable the model to generalize well across the task, by learning from the common patterns in the text while maintaining the ability to produce accurate task specific outputs. The performance of the model is evaluated by using the different datasets for each task, that achieve high accuracy in classifying emotions, identifying violent language, and detecting hate speech, which demonstrate the effectiveness of LSTM architecture in managing diverse labeling schemes while maintaining task specific precision. This research highlights the potential of multi-task learning by improving the efficiency and accuracy of text classification model like content moderation and sentimental analysis.