What question did this study set out to answer?

The aim is to develop a robust framework for detecting mental health issues using text data.

June 29, 2026Open Access

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

Key Points

The aim is to develop a robust framework for detecting mental health issues using text data.
Compiled a multi-source dataset of over 265,000 records from Reddit and public repositories.
Trained models including Logistic Regression, SVM, XGBoost, and CNN+LSTM architecture.
Evaluated the Voting Classifier to assess model accuracy.
The Voting Classifier achieved the highest accuracy of 83.68%.
The CNN+LSTM model achieved an accuracy of 78.34%.

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%.

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