2026· Proceedings of the 15th International Conference on Data Science, Technology and Applications· pp. 613-620· 0 citations· 22 references
TL;DR
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.
Abstract
: The increasing global burden of anxiety and depression necessitates innovative computational approaches for early, non-intrusive detection. Social media platforms offer naturalistic data where individuals voluntarily express psychological states through text. This paper presents 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. We systematically compare traditional machine learning models (Logistic Regression, SVM, Random Forest) and deep learning architectures (BiLSTM with Attention, fine-tuned BERT) across three benchmark datasets: SMHD, Dreaddit, and CLPsych 2015. Fine-tuned BERT achieves the highest macro-averaged F1 scores of 0.918, 0.909, and 0.862 respectively, outperforming traditional models by 7.1–17.3%. SHAP analysis identifies negative emotion, past-focused language, and somatic markers as the most predictive features. Cross-dataset validation reveals 15–25% performance degradation, highlighting the need for domain adaptation. Ethical considerations including false-positive risks and the gap between linguistic detection and clinical validity are discussed.
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
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.
D. D, S. S, L. K et al.· 2026 4th International Confe...· 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
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
Early detection of depression is a critical challenge for global mental health, as traditional clinical assessments are often delayed by social stigma and limited medical resources. While social media offers a unique window into real-world behavior, existing automated methods often lack clinical interpretability and symbolic reasoning. This paper proposes a novel framework that integrates a formal depression ontology with Large Language Models (LLMs) to enhance screening accuracy and transparency. By transforming ontological hierarchies and deterministic inference rules into a natural language style representation, we enable LLMs to perform step-by-step symbolic reasoning beyond simple sentiment analysis. Experimental results on a dataset of 200 Twitter users demonstrate that our ontology-driven framework significantly outperforms the baseline, achieving an accuracy of 88.0% and a precision of 91.5%. The model successfully reduces false positives by verifying temporal persistence and providing a traceable reasoning chain from digital footprints to clinical symptoms. This approach offers a promising path toward explainable AI-assisted mental health monitoring.
Zheqi Shen, Incheon Paik· 2026 6th International Confe...· 0 citations
Early identification of depression risk from social media text can support large-scale screening and timely follow-up. However, posts are often emotionally complex and linguistically ambiguous, which makes robust detection challenging. This paper proposes RST-DS (Rhetorical Structure Theory-based Depression Scanning), a discourse-aware framework that integrates Rhetorical Structure Theory (RST) signals with lexical evidence for classifying posts as Depressed/Non-Depressed (D/ND). Using Reddit posts, we compute an RST-derived score capturing rhetorical relationships and coherence and fuse it with lexical features represented via Term Frequency–Inverse Document Frequency using two strategies: feature addition (+) and feature concatenation (||). We evaluate K-Nearest Neighbors (KNN), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP), along with a soft-voting ensemble model named MLPBoostReg (an ensemble of LR, XGBoost, and MLP). Using a 5-fold cross validation, the concatenation strategy consistently outperforms the addition strategy across accuracy, precision, recall, and F1-score. The best-performing configuration, MLPBoostReg|| achieves the highest accuracy, precision, recall, and F1-score of 0.970, indicating a strong balance between identifying depression-related content and limiting false alarms. These findings suggest that combining rhetorical structure with lexical evidence improves depression detection from social media text, while noting that social-media labels do not substitute for clinical diagnosis.
Sahar Ajmal, Muhammad Shoaib, Faiza Iqbal et al.· Algorithms· 0 citations
