Synopsis
Mental health disorders represent a growing concern in contemporary society, often remaining undiagnosed due to the subtlety of their symptoms and the stigma surrounding psychological conditions. This project, titled Mental Health Prediction Using Natural Language Sentiment and Facial Expression Recognition, presents a comprehensive, automated framework that leverages advancements in natural language processing and computer vision to identify potential indicators of mental health disturbances. The proposed system adopts a dual-modality approach by analysing both textual data and facial expressions to assess emotional and psychological states. Sentiment analysis is performed on user-generated textual input, extracting emotional cues and evaluating the polarity and intensity of sentiments expressed. Simultaneously, facial expression recognition is employed to decode non-verbal signals using facial landmarks and emotion classification models. By integrating insights from these two modalities, the system enhances the reliability & depth of mental health assessment. This methodology aims to assist mental health professionals by providing a non-invasive, real-time, and scalable solution.
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Mental Health Prediction Using Sentiment Analysis and Facial Recognition
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Mental Health Prediction Using Sentiment Analysis and Facial Recognition
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Mental Health Prediction Using Sentiment Analysis & Facial Recognition
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Mental Health Prediction Using Sentiment Analysis & Facial Recognition
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Mental health disorders represent a growing concern in contemporary society, often remaining undiagnosed due to the subtlety of their symptoms and the stigma surrounding psychological conditions. This project, titled Mental Health Prediction Using Natural Language Sentiment and Facial Expression Recognition, presents a comprehensive, automated framework that leverages advancements in natural language processing and computer vision to identify potential indicators of mental health disturbances. The proposed system adopts a dual-modality approach by analysing both textual data and facial expressions to assess emotional and psychological states. Sentiment analysis is performed on user-generated textual input, extracting emotional cues and evaluating the polarity and intensity of sentiments expressed. Simultaneously, facial expression recognition is employed to decode non-verbal signals using facial landmarks and emotion classification models. By integrating insights from these two modalities, the system enhances the reliability & depth of mental health assessment. This methodology aims to assist mental health professionals by providing a non-invasive, real-time, and scalable solution. 116 pp. Englisch. Seller Inventory # 9783659802713
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Paperback. Condition: new. Paperback. Mental health disorders represent a growing concern in contemporary society, often remaining undiagnosed due to the subtlety of their symptoms and the stigma surrounding psychological conditions. This project, titled Mental Health Prediction Using Natural Language Sentiment and Facial Expression Recognition, presents a comprehensive, automated framework that leverages advancements in natural language processing and computer vision to identify potential indicators of mental health disturbances. The proposed system adopts a dual-modality approach by analysing both textual data and facial expressions to assess emotional and psychological states. Sentiment analysis is performed on user-generated textual input, extracting emotional cues and evaluating the polarity and intensity of sentiments expressed. Simultaneously, facial expression recognition is employed to decode non-verbal signals using facial landmarks and emotion classification models. By integrating insights from these two modalities, the system enhances the reliability & depth of mental health assessment. This methodology aims to assist mental health professionals by providing a non-invasive, real-time, and scalable solution. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9783659802713
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Mental Health Prediction Using Sentiment Analysis & Facial Recognition
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Mental Health Prediction Using Sentiment Analysis & Facial Recognition
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Mental Health Prediction Using Sentiment Analysis & Facial Recognition
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Mental Health Prediction Using Sentiment Analysis & Facial Recognition
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Taschenbuch. Condition: Neu. Mental Health Prediction Using Sentiment Analysis & Facial Recognition | Abhishek Roy (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9783659802713 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Seller Inventory # 133924111
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Mental Health Prediction Using Sentiment Analysis & Facial Recognition
Published by
LAP LAMBERT Academic Publishing, 2025
ISBN 10: 3659802719
ISBN 13: 9783659802713
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Mental health disorders represent a growing concern in contemporary society, often remaining undiagnosed due to the subtlety of their symptoms and the stigma surrounding psychological conditions. This project, titled Mental Health Prediction Using Natural Language Sentiment and Facial Expression Recognition, presents a comprehensive, automated framework that leverages advancements in natural language processing and computer vision to identify potential indicators of mental health disturbances. The proposed system adopts a dual-modality approach by analysing both textual data and facial expressions to assess emotional and psychological states. Sentiment analysis is performed on user-generated textual input, extracting emotional cues and evaluating the polarity and intensity of sentiments expressed. Simultaneously, facial expression recognition is employed to decode non-verbal signals using facial landmarks and emotion classification models. By integrating insights from these two modalities, the system enhances the reliability & depth of mental health assessment. This methodology aims to assist mental health professionals by providing a non-invasive, real-time, and scalable solution. Seller Inventory # 9783659802713