Search
3 articles for “Emotion-Sensitive NLP”
-
Reward-Aligned Reinforcement Learning from Human Feedback for Emotion-Sensitive Large Language Model Therapists: Balancing Empathetic Engagement, Boundary Safety, and Clinical Accountability
Abstract: The deployment of large language models (LLMs) in mental health therapy contexts introduces a critical alignment challenge: these systems must simultaneously cultivate genuine empathic rapport, observe clinically grounded safety boundaries, and remain auditable under institutional accountability frameworks. Existing reinforcement learning from human feedback (RLHF) pipelines optimize for a scalar reward signal that is demonstrably insufficient for the multi-objective, temporally extended nature of therapeutic conversation. This paper presents RA-RLHF-T (Reward-Aligned RLHF …
Published in International Journal of Tropical Medicines · Vol. 3, Issue 2, 2026 Read article
-
The Influence of Artificial Intelligence on Literary Criticism
Abstract: Artificial Intelligence (AI) has emerged as a significant force in reshaping contemporary literary criticism. Traditionally, the interpretation of literature has relied primarily on human-centered methods such as close reading, contextual analysis, and theoretical framing. However, the integration of AI-based techniques including Natural Language Processing (NLP), Machine Learning (ML), stylometry, and sentiment analysis has introduced new possibilities for examining literary texts at both micro and macro levels. These computational approaches allow …
Published in International Journal of Trends in Humanities · Vol. 3, Issue 2, 2026 · pp. 22–29 Read article
-
Real-time Emotion-aware AI Counseling System with Memory Retention Polymer Composites
Abstract: The availability of mental health services is still a major barrier, with many individuals constrained by financial limitations, social stigma, and a shortage of accessible counselors. This work introduces an emotion-aware AI counselor designed to provide empathetic and personalized emotional support via voice-based interfaces. The system leverages Natural Language Processing (NLP) and sentiment analysis to detect emotional cues from speech and generate contextually appropriate, comforting responses. A key innovation is …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1395–1407 Read article