A Hybrid Machine Learning and Deep Learning Approach for Mental Health Detection using Text Analysis

This paper presents a hybrid framework combining machine learning, ensemble methods, and deep learning for mental health detection from text. A multi-source dataset of over 265,000 records was compiled from Reddit and public repositories. Models including Logistic Regression, SVM, XGBoost, and a CNN+LSTM architecture were trained and evaluated. The Voting Classifier achieved the highest accuracy of 83.68%, with the CNN+LSTM model achieving 78.34%.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-27
DOI
https://doi.org/10.5281/zenodo.20971534
Primary Topic
Mental Health via Writing
Type
article
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article

A Hybrid Machine Learning and Deep Learning Approach for Mental Health Detection using Text Analysis

Pratyush Mishra
Zenodo (CERN European Organization for Nuclear Research)
Mental Health via Writing
article

A Hybrid Machine Learning and Deep Learning Approach for Mental Health Detection using Text Analysis

Pratyush Mishra
article en

Abstract

This paper presents a hybrid framework combining machine learning, ensemble methods, and deep learning for mental health detection from text. A multi-source dataset of over 265,000 records was compiled from Reddit and public repositories. Models including Logistic Regression, SVM, XGBoost, and a CNN+LSTM architecture were trained and evaluated. The Voting Classifier achieved the highest accuracy of 83.68%, with the CNN+LSTM model achieving 78.34%.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 4%
Mental Health via Writing
DOIOpen Access PDFOpenAlex
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A Hybrid Machine Learning and Deep Learning Approach for Mental Health Detection using Text Analysis — Pratyush Mishra · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS