Comparative Analysis of CNN Based Depression Detection Techniques for Social Media Text
Keywords:
Depression Classification, RNN , Social Media Text , Glove Embedding , Twitter Dataset , Mental Health.Abstract
The address of depression through social media analysis has emerged as a promising field, which can lead to an early identity of mental health concerns using signs of natural language. This work studies the existing work in the field of depression analysis and provides brief literature review on machine learning techniques and compares the result achieved with social media text. On the basis of review proposes a novel approach, for Depression Detection using better recurrent neural architecture using gloves embedding. The study examines a promotional recruitment neural network (RNN) model designed to classify depressive manifestations in user-related materials. By integrating the gloves (global) embedding, the model captures the deep semantic relationships and micro-linguistic characteristics prevalent in the social media text. The combinations greatly enrich the understanding of the subtle emotional signals associated with depression and the model of linguistic patterns. A Twitter-based training was used using a dataset and a twitter was evaluated on a dataset to ensure a strong cross-platform generality. The proposed model achieved an impressive test accuracy of 98.45%, showing its effectiveness and ability as a reliable tool to detect automatic depression in real -world social media applications..
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