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Natural Language Processing in Mental Health Care

Edited by Sudesh Yadav, Pethuru Raj, Satya Prakash Yadav, and Victor Hugo C. de Albuquerque
Copyright: 2026   |   Expected Pub Date:2026/08/30
ISBN: 9781394392841  |  Hardcover  |  
336 pages
Price: $225 USD
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One Line Description
Move to the forefront of psychiatric technology with a deep dive into the latest advancements in clinical natural language processing, providing the data-driven precision needed to scale mental health resources effectively.

Audience
Mental health specialists, academics, AI engineers and data scientists, IT professionals, and business executives exploring significant advancements in digital technologies and how next-generation innovations can enable cognitive and behavioral natural language processing in mental health.

Description
Mental health is one of the most significant public health concerns facing healthcare professionals today. The World Health Organization estimates that every year, 700,000 individuals die by suicide worldwide. With more people experiencing heightened anxiety and mental health pressure, health services are unable to keep up with demand. To deal with ever-increasing mental health problems worldwide, natural language processing proves a vital resource to medical practitioners. This book offers a comprehensive exploration of recent behavior-based natural language processing developments for mental health. It delves into methodologies and aspects of mathematical analysis, covering approaches such as behavioral signal processing in speech and language, speech analysis, clinical natural language processing, and rule-based chatbot approaches. By providing comprehensive coverage, the book equips readers with a deep understanding of the field to keep them abreast of the latest advancements.
Readers will find the volume:
• Explores new and innovative ideas of natural language processing in mental healthcare;
• Introduces various speech-processing techniques, behavioral detection techniques, and social media detection;
• Presents various mathematical approaches to the cognitive analysis of mental illness;
• Uncovers the impact and challenges of using various behavioral NLP techniques.

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Author / Editor Details
Sudesh Yadav, PhD is an Assistant Professor at the Govt. College, Ateli, Indira Gandhi University Meerpur, Rewari, Haryana, India. She has published many research papers in international journals and conferences. Her areas of interest include artificial intelligence, IoT, digital image processing, soft computing, and pattern recognition.

Pethuru Raj Chelliah, PhD is the chief architect in the Edge AI Division of Reliance Jio Platforms Ltd., Bangalore, India. He has published more than thirty research papers in peer-reviewed journals, authored and edited 42 books, and contributed more than 60 book chapters. He focuses on emerging technologies such as the Internet of Things, artificial intelligence model optimization, big and streaming data analytics, blockchain, and digital twins.

Satya Prakash Yadav, PhD is an Associate Professor in the Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, U.P., India, with more than 17 years of experience. He has published four books, two patents, and many research papers in international journals and conferences. His research interests include image processing, information retrieval, feature extraction and digital image processing.

Victor Hugo C. de Albuquerque, PhD is a Professor and Senior Researcher in the Department of Teleinformatics Engineering, Federal University of Ceará, Brazil. He serves as an editor and lead reviewer of several international journals. His interests include pattern recognition, artificial intelligence, image processing and analysis, and automation.

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Table of Contents
Preface
Part 1: Foundations of NLP in Mental Health
1. Introduction to NLP in Mental Health
Anandaraj S.P., Rakesh Mohanty, Trapty Agarwal, Taskeen Zaidi and Pratyashi Satapathy
1.1 Introduction to NLP’s Significance in Mental Health Analysis
1.2 Early Mental Health Condition Detection with NLP
1.3 Enhancing Mental Health Assessments with NLP
1.4 NLP-Based Customized Therapy Approaches
1.5 Advancements and Future Directions in Clinical NLP for Mental Health
1.6 Conclusion
References
2. Mental Health Issues, Concerns, and Solutions
Y. Narasimha Raja, Suvendu Narayan Mishra, Shweta Baja and M. N. Nachappa
2.1 Introduction
2.2 Psychological and Psychiatric Types
2.2.1 Depression
2.2.2 Anxiety Disorders
2.2.3 Posttraumatic Stress Disorder (PTSD)
2.2.4 Stigma
2.3 Mental Health Concerns
2.4 Potential Solutions for Mental Health
Early Detection and Intervention in Mental Health
Access to Mental Health Care
Solutions to Increase Access to MH Services
2.5 Conclusion
References
3. AI Technologies, Tools, and Industrial Use Cases
Kalpana K. Harish, Sasanka Choudhury, Awakash Mishra and Haripriya V.
3.1 Introduction
3.2 Key AI Technologies
3.3 AI Tools and Platforms
3.4 Industrial Applications of AI
3.5 Challenges in AI Adoption
3.6 AI’s Role in Sustainable Growth
3.7 Conclusion
References
4. NLP Techniques, Tools, and Use Cases of Speech Tagging
Kuppala Saritha, Prabhat Kumar Sahu, Trapty Agarwal and Ganesh D.
4.1 Introduction
4.2 Challenges in POS Tagging
4.2.1 Correctness Issues
4.2.2 False Positives
4.2.3 Handling Unknown Words
4.2.4 Ambiguity in Word Meanings
4.3 Advancement in ML and DL for POS Tagging
4.4 Various ML and DL Tools are Used for POS Tagging
ML-Based Tools
DL-Based Tools
4.5 Performance Evaluation
4.6 Application of POS Tagging
Machine Translation
Sentiment Analysis
Information Retrieval
4.7 Recommendations for Enhancing ML and DL Based Tool for POS Tagging
4.8 Conclusion
References
Part 2: AI and Machine Learning for Behavioral Understanding
5. Classification and Regression Algorithms in Behavioral NLP
Selvaraj Poornima, Manoranjan Parhi, Smita Mishra and Raghavendra R.
5.1 Introduction
5.2 Methodology
5.2.1 Data Collection
5.2.2 Classification and Regression Algorithm
5.2.3 Naïve Bayes (NB)
5.2.4 Support Vector Machine (SVM)
5.2.5 Logistic Tegression (LR)
5.3 Result
5.3.1 Emotional Analysis in Behavioral NLP: Anger, Fear, Joy and Sadness
5.3.1.1 Anger
5.3.1.2 Fear
5.3.1.3 Joy
5.3.1.4 Sadness
5.3.1.5 Discussion
5.4 Conclusion
References
6. Deep Learning Algorithms for Behavioral Understanding of Mental Health
Balamurugan S., M. Chandra Sekhar, Jitendriya Biswal, Savita and Sarbeswar Hota
6.1 Introduction
6.2 Methodology
6.2.1 Dataset
6.2.2 DL Techniques for Mental Health Behavior Analysis
6.2.3 Convolutional Neural Network (CNN)
6.2.4 Autoencoder
6.2.5 Long Short-Term Memory
6.2.6 Generative Adversarial Networks
6.2.7 Gated Recurrent Unit
6.2.8 Bidirectional LSTM
6.3 Results
6.3.1 Comparative Analysis of DL-Based Approaches
6.3.2 DL-Based Methods for Analyzing Behavioral Patterns in Mental Health Disorders
6.3.3 Discussion
6.4 Conclusion
References
7. Mental Health Detection Using Natural Language Processing
Murugan R., Syed Siraj Ahmed, Snehanshu Dey, Shweta Bajaj and Bichitrananda Patra
7.1 Introduction
7.2 Methodology
7.3 Result
7.4 Discussion
7.5 Conclusion
References
8. Behavioral Signal Processing
Rengarajan A., Raghavendra T. S., Praveen Priyaranjan Nayak and Trapty Agarwal
8.1 Introduction
8.2 Methodology
8.2.1 Data Collection
8.2.2 AI Methods for Behavior Signal Processing
8.2.3 Physiological Signal Using EEG
8.2.4 Harmony Search Algorithm-Long Short-Term Memory
8.2.5 ResNet
8.2.6 DenseNet
8.2.7 Random Forest
8.2.8 Speech Emotion Recognition (SER) Using Speech Signal
8.2.9 XGBoost
8.2.10 DNN
8.2.11 RCNN
8.2.12 1-D DCNN
8.3 Result
8.3.1 Comparison Evaluation of ML and DL-Based Methods
8.3.1.1 Accuracy
8.3.1.2 Precision
8.3.1.3 Recall
8.3.1.4 F1 Score
8.3.1.5 EEG Signal
8.3.1.6 Speech Signal
8.3.2 Discussion
8.4 Conclusion
References
9. Prediction of Suicidal Thoughts and Pychiatric Symptoms
Manju Bargavi S. K., Y. Narasimha Raja, Surjeet Sahoo and Trapty Agarwal
9.1 Introduction
9.2 Methodology
9.2.1 Datasets
9.2.2 Data Preparation for the Classification Model
9.2.3 Text Analysis Method
9.2.4 Bidirectional Encoder Representation from Transformer
9.2.5 Robustly Optimized BERT Pretraining Approach (RoBERTa)
9.2.6 Logistic Regression
9.2.7 Long Short-Term Memory (LSTM)
9.2.8 Image Analysis Methods
9.2.9 Convolutional Neural Networks (CNN)
9.2.10 Support Vector Machine (SVM)
9.3 Result
9.3.1 Discussion
9.4 Conclusion
References
Part 3: Advanced Methods and Evaluation Techniques
10. Evaluation of Model Performance—Confusion Matrices, Sensitivity, Specificity, Kappa Statistics, Precision, Recall F-Measure, ROC Curve, Etc.
Kamalraj R., Akkamahadevi C., Duryodhan Jena and Aditya Yadav
10.1 Introduction
10.2 Evaluation Metrics
10.3 Conclusion
References
11. Methods of Pruning and Tagging
Awakash Mishra, Gobi N., Pallavi M. and Avinash Samantra
11.1 Introduction
11.2 Purpose of Pruning in Mental Healthcare NLP
11.3 Tagging’s Significance in Mental Healthcare NLP
11.4 Conclusion
References
12. Social Media for Depression Detection in Mental Health
Smita Mishra, Febin Prakash, Rosan Basha and Chinmaya Kumar Mohapatra
12.1 Introduction
12.2 Methodology
12.2.1 Dataset
12.2.2 Data Preparation
12.2.3 Social Media Depression Detection Models
12.2.4 Gradient Boosting Decision Tree
12.2.5 Naive Bayes
12.2.6 Random Forest
12.2.7 Decision Tree
12.2.8 Logistic Regression
12.2.9 Bidirectional Long Short-Term Memory
12.3 Result
12.3.1 Performance Analysis
12.3.2 Discussion
12.4 Conclusion
References
13. Creation of Cognitive Behavioral Chatbots for Mental Health
Monika Joshi, Pushpa J., Sharon M. and Manoranjan Dash
13.1 Introduction
13.2 Methodology
13.3 Results and Discussion
13.4 Conclusion
References
Part 4: Future Directions in Behavioral NLP
14. The Future of Behavioral Natural Language Processing in Healthcare
Monika Joshi, Nanthini K., Devi S. and Biswaranjan Swain
14.1 Introduction
14.2 Natural Language Processing
14.3 Factors Behind NLP in Healthcare Systems
14.4 Technological Advancements in Behavioral NLP
14.5 Applications of NLP in Healthcare
14.6 Benefits of NLP in Healthcare
14.7 Future Behavioral Natural Language Processing in Healthcare
14.8 Discussion
14.9 Conclusion
References
15. Medical Graph RAG: Toward Safe Medical Large Language Model via Graph Retrieval-Augmented Generation
Trapty Agarwal, Ramkumar Krishnamoorthy, Praveena K. N., Adya Kinkar Panda and Ayasa Kanta Mohanty
15.1 Introduction
15.2 Methodology
15.2.1 Dataset
15.2.2 Testing Data
15.2.3 Medical Graph Retrieval-Augmented Generation (Med Graph RAG)
15.2.4 Large Language Models
15.3 Results
15.3.1 Performance Metrics
15.3.2 Baselines with Different Retrievals for Medical Large Language Models
15.3.3 Discussion
15.4 Conclusion
References
16. Real-Time Cognitive Load Prediction in Digital Environments
Monika Joshi, Pushpa J., Sharon M. and Manoranjan Dash
16.1 Introduction
16.2 Methodology
16.2.1 Dataset
16.3 Result and Discussion
16.3.1 Performance Evaluation
16.3.2 Discussion
16.4 Conclusion
References
17. NLP-Based Gamification Approaches for Personalized Mental Wellness Support
Samir Sahu, Trapty Agarwal, Sanjeev Kumar Mandal, Deepthi S. and Tapasmini Sahoo
17.1 Introduction
17.2 Psychological Principles and Theoretical Frameworks
17.3 NLP Methods for Personalized Mental Health
17.4 Techniques for Gamification to Increase User Engagement
17.5 Applicants and Difficulties for Gamified Health Interventions
17.6 Conclusion
References
18. Context-Aware NLP for Early Detection of Cognitive and Emotional Distress
Chandan Das, Awakash Mishra, Preethi D. and Mohammed Mujeerulla
18.1 Introduction
18.2 Methodology
18.2.1 Dataset
18.2.2 Data Preparation
18.2.3 Early Detection Models for Emotional Distress
18.2.4 Multi-Domain Network
18.2.5 Support Vector Machine
18.2.6 Random Forest
18.2.7 Multilayer Perceptron Classifier
18.2.8 Gated Recurrent Unit
18.3 Results
18.3.1 Comparative Analysis
18.3.2 Discussion
18.4 Conclusion
References
19. Deep Learning Techniques for Analyzing Mood Variability in Patients
Pritam Keshari Sahoo, Awakash Mishra, Suneetha K., Smitha S. P. and Laxmidhar Maharana
19.1 Introduction
19.2 Methodology
19.2.1 Dataset
19.2.2 Data Preprocessing Steps
19.2.3 Convolutional Neural Networks and Bidirectional Long Short-Term Memory (CNN-BiLSTM)
19.2.4 Convolutional Neural Networks (CNN)
19.2.5 Extra Long Transformer Network (XLNet)
19.2.6 Residual Network
19.2.7 Bidirectional Long Short-Term Memory
19.3 Results
19.4 Discussion
19.5 Conclusion
References
20. Text-Based Cognitive Bias Identification for Assessing Negative Thinking Patterns
Jyoti Ranjan Das, Savita, Kannagi Anbazhagan and Sridevi S.
20.1 Introduction
20.2 Methodology
20.2.1 Dataset
20.2.2 NLP Techniques
20.2.3 Global Vectors for Word Representation
20.2.4 Count Vectorization
20.2.5 Bigrams
20.2.6 Negative Thinking Patterns Using ML and DL Techniques
20.2.7 Long Short-Term Memory
20.2.8 Convolutional Neural Network
20.2.9 Logistic Regression
20.2.10 Support Vector Machine
20.2.11 Bidirectional LSTM
20.2.12 Random Forest
20.3 Results
20.3.1 Discussion
20.4 Conclusion
References
Index

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Description
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Table of Contents
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