Hybrid Capsule-CNN Framework for Sentiment-Depending Depression Identification

This research work offers a hybrid Capsule-CNN framework that is very effective in identifying depressed users from a user review sentiment classification based task. The model, which fuses the characteristics of Capsule Networks and Convolutional Neural infrastructure, has the capacity to comprehend the intricate spatial relations and extract hierarchical features from the text. The Convolutional Neural infrastructure is to intuitively locate and communicate the positivity and negativity of the emotions, while the Capsule Network is responsible for the implementation of the contextual dependencies and the orientation of the features, which are certainly not fully addressed by the traditional deep learning models. These two architectures mutually reinforce the more comprehensive and accurate understanding of the subtle emotional expressions in the reviews. The system is tried and proven on the datasets of real-world reviews that disclose the symptoms of depression in the user-generated content. This current system of tackling is non-invasive but moreover has the potential to become one of the revolutionary steps in the mental health field, for instance, deep learning-powered assistance in recognizing mental diseases at an early stage.