A cloud-based two-layer text classification framework for mental health screening with sarcasm and emoji-aware sentiment analysis.

Abstract

The increasing use of digital communication platforms has led individuals to express emotions and mental health concerns through text containing implicit emotional cues, informal language, and non-standard expressions. Traditional sentiment analysis systems often struggle to capture these contextual nuances, limiting their effectiveness in mental health-related text analysis . To address this challenge, this study proposes a two-layer framework that combines Azure Sentiment Analysis and Azure Custom Text Classification for sentiment and mental health-related text categorization. In the first layer, user-generated text is classified into positive, neutral, or negative sentiment categories using Azure Sentiment Analysis. Text identified as negative is subsequently analysed using Azure Custom Text Classification to categorize content into predefined mental health-related classes, including Anxiety, Depression, PTSD, Social Anxiety Disorder, and Suicidal Ideation and Behaviour. The proposed framework aims to provide a structured approach for identifying linguistic patterns associated with mental health-related discussions and supporting mental health screening and triage applications. Experimental evaluation using an 80% training and 20% testing split achieved an overall Precision, Recall, and F1-score of 96.97%. Class-level evaluation demonstrated strong performance across multiple categories, with F1-scores ranging from 0.94 to 1.000. The findings indicate that the proposed architecture can effectively classify mental health-related textual content within the evaluated dataset while providing a scalable framework for automated sentiment and text classification. The study contributes to the growing field of intelligent emotional computing and highlights the potential of cloud-based natural language processing tools for mental health-related text analytics . The reported results are limited to the evaluated dataset and should be interpreted as a text classification and screening approach rather than a clinical diagnostic system. This manuscript presents the computational component of a broader mixed-methods study registered under CTRI/2024/06/068766, titled "Exploring Mental Health Status in a Selected Population: A Corpus Analysis Combining Forensic Linguistics and Psychology - a Mixed Method Study." The current work focuses on the development and validation of an AI-based diagnostic tool for mental health assessment using synthetic and anonymized textual data, constituting a secondary objective of the registered protocol. Registry: Clinical Trials Registry- India (CTRI) Trial Registration Number: CTRI/2024/06/068766 Date of Registration: 12.06.2024.