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Deep Learning for Depression Detection from Textual Data - 2022

Deep Learning For Depression Detection From Textual Data

Research Paper on Deep Learning For Depression Detection From Textual Data

Research Area:  Machine Learning


Depression is a prevalent sickness, spreading worldwide with potentially serious implications. Timely recognition of emotional responses plays a pivotal function at present, with the profound expansion of social media and users of the internet. Mental illnesses are highly hazardous, stirring more than three hundred million people. Moreover, that is why research is focused on this subject. With the advancements of machine learning and the availability of sample data relevant to depression, there is the possibility of developing an early depression diagnostic system, which is key to lessening the number of afflicted individuals. This paper proposes a productive model by implementing the Long-Short Term Memory (LSTM) model, consisting of two hidden layers and large bias with Recurrent Neural Network (RNN) with two dense layers, to predict depression from text, which can be beneficial in protecting individuals from mental disorders and suicidal affairs. We train RNN on textual data to identify depression from text, semantics, and written content. The proposed framework achieves 99.0% accuracy, higher than its counterpart, frequency-based deep learning models, whereas the false positive rate is reduced. We also compare the proposed model with other models regarding its mean accuracy. The proposed approach indicates the feasibility of RNN and LSTM by achieving exceptional results for early recognition of depression in the emotions of numerous social media subscribers.

Deep Learning
Depression Detection
Textual Data
machine learning
Recurrent Neural Network

Author(s) Name:  Amna Amanat,Muhammad Rizwan,Abdul Rehman Javed ,Maha Abdelhaq,Raed Alsaqour,Sharnil Pandya and Mueen Uddin

Journal name:  Electronics

Conferrence name:  

Publisher name:  MDPI

DOI:  10.3390/electronics11050676

Volume Information:  Volume 11, Issue(5)