The Evaluation of Deep Learning Models for Detecting Mental Disorders Based on Text Summarization in Societal Analysis
Keywords:
LSTM, XLNet, GRU, mental disorders, sentiment analysis and Twitter.Abstract
Stress significantly affects societal communications, causing misunderstandings, strained relationships, and conflicts due to heightened emotional sensitivity. Stressed individuals may respond less empathetically, leading to negative interactions and potentially influencing public discourse. To address this, promoting emotional intelligence, recognizing stress indicators, and encouraging empathetic communication can mitigate these challenges and foster healthier societal interactions. Identifying signs of stress can often be challenging due to the subjective and multifaceted nature of stress and the various ways individuals express it. Furthermore, traditional methods of stress detection, such as self-reporting or physiological measures, may not be feasible or accessible for all individuals. By leveraging advancements in AI, we aim to develop a predictive model that can accurately identify stress levels in individuals based on textual data. Stress Scan is a cutting-edge project at the intersection of artificial intelligence, natural language processing, and mental health, focusing on predictive modelling for human stress detection in textual content through the deployment of advanced deep learning models. Leveraging the power of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM, and XLNet architectures, Stress Scan aims to revolutionize stress assessment in the digital age. By harnessing the nuances of language, sentiment, and context, the project pioneers a comprehensive approach to automatically identify and classify stress levels in text, sourced from diverse platforms like social media, chat conversations, and emails. Through meticulous data preprocessing, feature extraction, and model training on a carefully curated dataset encompassing a spectrum of stress expressions, Stress Scan’s deep learning models learn intricate linguistic patterns and emotional cues, leading to unparalleled accuracy in stress detection. The versatility of these models offers real-time stress monitoring for individuals, insights for mental health professionals, and an organizational tool for assessing and mitigating workplace stress. Stress Scan encapsulates a groundbreaking endeavour to enhance mental health awareness and support using advanced deep learning models, contributing to a more resilient and well-balanced digital society.
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Yerragudipadu subbarayudu , alladi Sureshbabu “Distributed Multimodal Aspective on Topic Model Using Sentiment Analysis for Recognition of Public Health Surveillance” Expert Clouds and Applications, 16 July 2021, DOI: https://doi.org/10.1007/978-981-16-2126-0_38 Springer, Singapore Print ISBN 978-981-16-2125-3 Online ISBN 978-981-16-2126-0
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