Acta Scientific Computer Sciences

Research Article Volume 5 Issue 4

Detecting Signs of Depression from Social Media Platforms

Govind Anjan* and Alladi Srikar

School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

*Corresponding Author: Govind Anjan, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.

Received: February 16, 2023; Published: March 23, 2023

Abstract

Depression is a common mental illness that involves sadness and lack of interest in all day-to-day activities. Detecting depression is important since it must be observed and treated at an early stage to avoid severe consequences. Our Project aims to detect the signs of depression of a person from their social media postings wherein people share their feelings and emotions. Given social media postings in English, our system should classify the signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed”. To build a sophisticated classification system we use different Neural Network models like Long Short Term Memory (LSTM), Bi-Directional LSTM, Convolutional neural network (CNN), Gated recurrent units(GRU). On developing all the models we obtained the highest train accuracy of 98.8 percent and test accuracy of 48 percent.

Keywords: Online Social Media; Depression; Machine Learning; Deep Learning

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Citation

Citation: Govind Anjan and Alladi Srikar. “Detecting Signs of Depression from Social Media Platforms".Acta Scientific Computer Sciences 5.4 (2023): 74-79.

Copyright

Copyright: © 2023 Govind Anjan and Alladi Srikar. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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