research-article

Depression Detection from Social Media Text Analysis using Natural Language Processing Techniques and Hybrid Deep Learning Model

Authors:

Vankayala Tejaswini,

Korra Sathya Babu,

Bibhudatta SahooAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 23, Issue 1

Article No.: 4, Pages 1 - 20

https://doi.org/10.1145/3569580

Published: 15 January 2024 Publication History

Abstract

Depression is a kind of emotion that negatively impacts people's daily lives. The number of people suffering from long-term feelings is increasing every year across the globe. Depressed patients may engage in self-harm behaviors, which occasionally result in suicide. Many psychiatrists struggle to identify the presence of mental illness or negative emotion early to provide a better course of treatment before they reach a critical stage. One of the most challenging problems is detecting depression in people at the earliest possible stage. Researchers are using Natural Language Processing (NLP) techniques to analyze text content uploaded on social media, which helps to design approaches for detecting depression. This work analyses numerous prior studies that used learning techniques to identify depression. The existing methods suffer from better model representation problems to detect depression from the text with high accuracy. The present work addresses a solution to these problems by creating a new hybrid deep learning neural network design with better text representations called “Fasttext Convolution Neural Network with Long Short-Term Memory (FCL).” In addition, this work utilizes the advantage of NLP to simplify the text analysis during the model development. The FCL model comprises fasttext embedding for better text representation considering out-of-vocabulary (OOV) with semantic information, a convolution neural network (CNN) architecture to extract global information, and Long Short-Term Memory (LSTM) architecture to extract local features with dependencies. The present work was implemented on real-world datasets utilized in the literature. The proposed technique provides better results than the state-of-the-art to detect depression with high accuracy.

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Dalal SJain SDave M(2025)Review of Advancements in Depression Detection Using Social Media DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.344862412:1(77-100)Online publication date: Feb-2025
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Tahir WKhalid SAlmutairi SAbohashrh MMemon SKhan J(2025)Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning TechniquesIEEE Access10.1109/ACCESS.2025.353086213(12789-12818)Online publication date: 2025
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Index Terms

Depression Detection from Social Media Text Analysis using Natural Language Processing Techniques and Hybrid Deep Learning Model
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 23, Issue 1

January 2024

385 pages

EISSN:2375-4702

DOI:10.1145/3613498

Editor:
Imed Zitoun
Google, USA

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 15 January 2024

Online AM: 05 November 2022

Accepted: 25 September 2022

Revised: 07 September 2022

Received: 08 July 2022

Published in TALLIP Volume 23, Issue 1

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Yu PZhou LZhou SJiao JHuang GLu P(2025)Physics-Constrained Three-Dimensional Swin Transformer for Gravity Data InversionRemote Sensing10.3390/rs1701011317:1(113)Online publication date: 1-Jan-2025
https://doi.org/10.3390/rs17010113
Dalal SJain SDave M(2025)Review of Advancements in Depression Detection Using Social Media DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.344862412:1(77-100)Online publication date: Feb-2025
https://doi.org/10.1109/TCSS.2024.3448624
Tahir WKhalid SAlmutairi SAbohashrh MMemon SKhan J(2025)Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning TechniquesIEEE Access10.1109/ACCESS.2025.353086213(12789-12818)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3530862
Karamat AImran MYaseen MBukhsh RAslam SAshraf N(2025)A Hybrid Transformer Architecture for Multiclass Mental Illness Prediction Using Social Media TextIEEE Access10.1109/ACCESS.2024.351930813(12148-12167)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2024.3519308
Selim MAssiri M(2025)Enhancing Arabic text-to-speech synthesis for emotional expression in visually impaired individuals using the artificial hummingbird and hybrid deep learning modelAlexandria Engineering Journal10.1016/j.aej.2025.02.011119(493-502)Online publication date: Apr-2025
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Alsaedi AYafooz W(2025)Early Depression Detection from Social Media: State-of-the-Art ApproachesAI-Driven: Social Media Analytics and Cybersecurity10.1007/978-3-031-80334-5_4(61-75)Online publication date: 4-Mar-2025
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Shah SAljawarneh MSaleem MJawarneh M(2024)Mental illness detection through harvesting social media: a comprehensive literature reviewPeerJ Computer Science10.7717/peerj-cs.229610(e2296)Online publication date: 7-Oct-2024
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