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Depression Severity Classification from Social Media Text using Natural Language Processing and Machine Learning

by Abhijeetsinh Jadeja, Priyanka Ameta, Deepika Ameta, Asha Patil
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 116
Year of Publication: 2026
Authors: Abhijeetsinh Jadeja, Priyanka Ameta, Deepika Ameta, Asha Patil
10.5120/ijca9769b912849a

Abhijeetsinh Jadeja, Priyanka Ameta, Deepika Ameta, Asha Patil . Depression Severity Classification from Social Media Text using Natural Language Processing and Machine Learning. International Journal of Computer Applications. 187, 116 ( Jun 2026), 27-31. DOI=10.5120/ijca9769b912849a

@article{ 10.5120/ijca9769b912849a,
author = { Abhijeetsinh Jadeja, Priyanka Ameta, Deepika Ameta, Asha Patil },
title = { Depression Severity Classification from Social Media Text using Natural Language Processing and Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2026 },
volume = { 187 },
number = { 116 },
month = { Jun },
year = { 2026 },
issn = { 0975-8887 },
pages = { 27-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number116/depression-severity-classification-from-social-media-text-using-natural-language-processing-and-machine-learning/ },
doi = { 10.5120/ijca9769b912849a },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-06-25T12:52:25.825056+05:30
%A Abhijeetsinh Jadeja
%A Priyanka Ameta
%A Deepika Ameta
%A Asha Patil
%T Depression Severity Classification from Social Media Text using Natural Language Processing and Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 116
%P 27-31
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With its potential for early diagnosis, research on mental health monitoring is an active area and automatic analysis is an important component of such a system. However most research involves simply detecting presence/absence of depression, which is not sufficiently granular for practical application. We propose the development of a interactive chatbot which would classify user responses into four severity levels of depression-Minimal, Mild, Moderate and Severe. We developed an NLP pipeline using lemmatization and TF-IDF vectorization to train and compare a Logistic Regression model with a fine-tuned Support Vector Machine. Results indicate that the SVM model achieved 74.36% accuracy among other algorithms and could be used as a suitable engine to provide an interactive conversational interface to asses user's current stress level in real time.

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Index Terms

Computer Science
Information Sciences

Keywords

Depression Detection NLP Machine Learning Chatbot Severity Classification TF-IDF SVM SMOTE

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