Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 837-842· 0 citations· 22 references
Abstract
Mental illnesses like depression, anxiety, stress, and so on have become more widespread, and this has necessitated the availability of assessment tools that are readily available, scalable, and automated. The paper will offer a web-based mental health risk assessment system that utilizes the latest technology of Natural Language Processing (NLP) in real-time to analyze textual information provided by users. The offered system incorporates a hybrid deep learning framework with the relation of DeBERTa, BiLSTM, and XGBoost to promote the contextual comprehension, sequential emotional pattern identification, and effective classification of the performance. First, text input by the users is received with a secure web interface and processed with general NLP preprocessing methods, such as tokenization, lemmatization, and sentiment normalization. To extract deep semantic relationships in the text, DeBERTa is used to extract contextual embeddings. Such embeddings are also trained in the form of a Bidirectional Long Short-Term Memory (BiLSTM) network in order to capture emotional dynamics and linguistic reinforcing relations. Fused feature representation, sentiments, and linguistic indicators are input into an XGBoost classifier to predict mental health in multi-class. There is a weighted risk scoring system used to measure the level of severity and provide tailored feedback. It is experimentally tested on standard mental health text data sets that the proposed hybrid framework is more effective than the traditional machine learning and standalone transformer models in terms of accuracy, precision, recall, and F1-score. The architecture is scalable to the deployment of a web system, which is guaranteed to perform in real-time, secure data, and privacy of users. The suggested framework offers a solid and smart instrument to identify the risk of mental health early and help intervene in time and to promote the development of digital health care.
Social media has become a significant resource for evaluating public attitudes regarding mental health, as individuals may express their thoughts through postings, comments, and conversations. Sentiment analysis is a promising method for studying emotional patterns expressed in mental-health-related text. Traditional machine learning for sentiment analysis faces challenges like transparency, scarce labeled data, and language limitations. This paper presents a sentiment classification (positive, negative, neutral) framework applied to mental-health-related Reddit posts. Repeated emotional patterns can offer helpful insights into sentiment trends in mental health discussions, but they shouldn’t be seen as proof of clinical disorders. In this paper, we propose a hybrid framework for sentiment classification from text, termed QBERT-LSTM, which integrates quantum-enhanced bidirectional encoder representations from transformers (QBERT) with long short-term memory (LSTM) networks. In the proposed approach, transformer models excel at capturing global context, while the LSTM enhances sequential patterns and temporal features. The quantum component uses a parameterized circuit with angle encoding and variational gates to perform nonlinear transformations on a quantum simulator (PennyLane). Subsequently, the model is tested on more intricate circuits comprising 2, 4, 6, 8, 10, and 12 qubits to assess its consistency. A novel Reddit dataset of mental health-related subreddits (anxiety, panic attacks, and depression) is constructed, with sentiment labels derived from the VADER sentiment analyzer. We thoroughly assessed the performance of our proposed model against deep learning models and cutting-edge transformer architectures, achieving up to 98% accuracy in the best-performing configuration on the generated dataset (mean accuracy $0.97~\pm ~0.0059$ across five random seeds).
Najnin Sultana Shirin, Md. Aminul Islam, Maria Akter Abin et al.· IEEE Access· 0 citations
A comprehensive Natural Language Processing (NLP) pipeline for detecting linguistic correlates of anxiety and depression from social media text, a task distinct from clinical diagnosis is presented.
Muhammad Azhar, Adeen Amjad, Bilal Hussain et al.· Proceedings of the 15th Inte...· 0 citations
Mental health disorders such as depression, anxiety, and post-traumatic stress disorder (PTSD) affect over one billion
people worldwide, yet early detection remains a major clinical challenge. In recent years, text data from social media posts,
clinical notes, and patient surveys has emerged as a rich source of signals for automated mental health screening. However,
most existing machine learning models operate as black boxes, limiting clinical adoption. This paper presents an interpretable
machine learning framework that combines natural language processing (NLP) feature extraction with explainable AI
techniques — specifically SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic
Explanations) — to predict mental health conditions from text while providing transparent, clinically meaningful
explanations. A multi- class classification task involving depression, anxiety, PTSD, and healthy controls is performed on a
dataset of 19,320 labelled text samples. The proposed XGBoost model with SHAP explanations achieves 87.3% accuracy and
an AUC of 0.924, while the fine-tuned BERT model achieves 91.6% accuracy and an AUC of 0.961. Experimental results
demonstrate that interpretability does not significantly compromise predictive performance, enabling trustworthy AI-assisted
mental health screening.
N. Thakur, D. Patil, Pushpa Choudhary· International Journal for Re...· 0 citations
Recent advances in machine learning and natural language processing (NLP) have enabled the early identification of mental disorders from social media content. Among such platforms, Reddit is characterized by linguistically rich, long-form narratives in which users describe their psychological symptoms, emotions, and personal experiences. However, these datasets present significant challenges for text classification because the posts are lengthy, voluminous, and class-imbalanced. In this study, we revisit the concept of a segmentation-based, expression-weighted representation that emphasizes clinically relevant language while suppressing irrelevant content. During this investigation, we identify a critical limitation in conventional preprocessing pipelines—specifically, the application of chunking and oversampling prior to the train–test split—which introduces data leakage and consequently inflates reported performance. To address this issue, we propose a leakage-safe evaluation protocol together with a domain-informed density extraction method that identifies clinically dense passages using a lexicon derived exclusively from the training data.
The proposed method was evaluated using logistic regression, linear SVM, XGBoost, fastText, RNN, and TextCNN, together with a transformer-based baseline (MentalBERT), all under a leakage-safe evaluation protocol. After eliminating data leakage, the apparent advantage of naive weighted chunking disappeared, and the headline macro-F1 score decreased from approximately 0.96 to 0.67. In contrast, the proposed domain-informed density extraction method consistently outperformed fixed-window chunking in five of the six models and matched or exceeded the full-text baseline in several cases. Statistically significant improvements were observed for logistic regression, RNN, and fastText, despite using only a fraction of the input text. For the context-limited transformer, density-based selection significantly outperformed naive truncation (macro-F1 + 0.023, 95% CI [+0.006, +0.041],
p
= 0.008).
Our findings demonstrate that a leakage-safe evaluation protocol is essential for producing credible results in mental health classification from long social media posts. They further show that domain-informed density extraction provides a robust and practical text representation, with its greatest benefits emerging when models are unable to process the entire post. By selectively preserving clinically informative content while reducing input length, the proposed approach offers an effective and computationally efficient alternative to increasingly complex model architectures.
Unknown authors· Frontiers in Artificial Inte...· 0 citations
Mental health diseases such as Depression, Anxiety, Post-Traumatic Stress Disorder (PTSD), attention deficit hyperactivity disorder (ADHD), bipolar disorder, etc., have become a major health issue worldwide. Limited access to clinical services and delayed diagnosis remain a challenge for early detection of many disorders. The fast expansion of social media platforms has led to large libraries of textual data generated by users, which can provide useful insight into psychological well-being. We propose a novel machine learning-driven predictive computational model based on DistilBERT for automated multi-class mental health identification from Reddit posts in this paper. The proposed framework comprises data preparation, tokenization, feature extraction, transformer-based contextual encoding and supervised classification. The experimental evaluation was performed on the Reddit Mental Health Dataset, which contains 13,727 samples and 2,746 testing samples. The proposed model has obtained an accuracy of 95.6%, a precision of 94.7%, a recall of 96.1% and an F1-score of 95.8%. Our results indicate that transformer-based models are robust and scalable solutions for real-world mental health testing and decision-support systems.
Radhey Shyam, Prashant Yadav, Pawan Thakur· International journal on eme...· 0 citations
The occurrence of events, such as data breaches, loss of sensitive information, and inappropriate use of an individual’s personal information often adversely impacts the mental health of users. Insightful analysis of such online complaint-based posts provides valuable insights into the mental health challenges faced by users. In this article, we present a novel deep learning model referred to as HEAL-MinD to detect mental health based on online user posts. The model begins with an input layer that processes the input text followed by the embedding layer which employs two types of embeddings to capture contextual and semantic information. A convolution gated and attention (CGAT) layer is then introduced which is one of the most important components of HEAL-MinD. In addition, a rich set of hand-crafted auxiliary features is incorporated into the model to develop it more accurate and informative. Finally, a dense layer performs the final classification, and the output layer produces the prediction by categorizing each input as either mental health or non-mental health. We conduct extensive experimental evaluations on three newly developed datasets to demonstrate the effectiveness of the HEAL-MinD model. The proposed model outperforms five existing studies and 12 baseline models. It achieves an F-score and accuracy of up to 95% and 96%, respectively, across all three datasets. The required source code to implement the HEAL-MinD model is available at GitHub:
https://github.com/vishxl/HEAL-MinD
.