CONTEXT-AWARE NATURAL LANGUAGE PROCESSING AND DEEP LEARNING SYSTEM FOR EMOTION RECOGNITION IN HUMAN-COMPUTER INTERACTION

Ritu Shree; Romil Jain; Akanksha Tiwari; Arun Kumar Choudhary; Sumitra Sangwan; Krishan Kumar

doi:10.29121/shodhkosh.v7.i7s.2026.7862

Authors

Ritu Shree Assistant Professor, Department of Computer Science and Engineering, Vivekananda Global University, Jaipur, Rajasthan, India
Romil Jain Assistant Professor, Department of Computer Science and Engineering, Vivekananda Global University, Jaipur, Rajasthan, India
Akanksha Tiwari Assistant Professor, Department of Electronics and Communication Engineering, Feroze Gandhi Institute of Engineering and Technology, Raebareli, Uttar Pradesh, India
Dr. Arun Kumar Choudhary Dean (Academics), Venkateshwara Open University, Itanagar, Andhra Pradesh, India
Dr. Sumitra Sangwan Assistant Professor, Department of Computer Science, K.T.G.C., Ratia, Fatehabad, Haryana, India
Dr. Krishan Kumar Associate Professor, Department of Information Technology, G L Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i7s.2026.7862

Keywords:

Context-Aware NLP, Emotion Recognition, Deep Learning, LSTM Networks, Human-Computer Interaction

Abstract [English]

Emotion recognition in human–computer interaction (HCI) is a complex yet essential task with wide-ranging applications in mental health monitoring, intelligent systems, and user experience enhancement. This research proposes a context-aware Natural Language Processing (NLP) and deep learning-based framework for accurate detection of emotional states (ES) from human communication. Unlike traditional approaches that rely solely on acoustic or lexical features, the proposed system integrates contextual semantics, linguistic patterns, and speech characteristics such as pitch, rhythm, and prosody to achieve a more comprehensive understanding of emotions. The model leverages hybrid deep learning techniques, combining transformer-based NLP models with Long Short-Term Memory (LSTM) networks to effectively capture both contextual meaning and temporal dependencies in data. Additionally, attention mechanisms are employed to highlight emotionally significant features, improving classification performance. The system is trained and evaluated on diverse, well-annotated datasets representing multiple emotional states, ensuring robustness and generalization. Experimental results demonstrate that the proposed approach outperforms conventional methods in terms of accuracy, precision, and reliability. This study contributes to the advancement of emotion-aware intelligent systems and offers promising applications in adaptive interfaces, virtual assistants, sentiment analysis, and psychological assessment.

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