Emotion Prediction With Chatbot: A Deep Learning Approach
Year: 2025
Type: article
Abstract: This paper presents a novel approach for emotion prediction using a chatbot interface,leveraging deep learning techniques to analyses textual input and predict user emotions in realtime. Traditional emotion detection methods rely on facial expressions or voice modulation,which may not be feasible in text-based interactions. The proposed system employs NaturalLanguage Processing (NLP) techniques, including Bidirectional Encoder Representations fromTransformers (BERT) for contextual understanding and Long Short- Term Memory (LSTM)networks for sequential emotion classification. The model is trained on a curated dataset ofconversational text labelled with emotional states (e.g., happiness, sadness, anger). Validationagainst human-annotated datasets demonstrates an accuracy of 92%, outperforming existingrule-based and machine learning approaches. This research contributes to enhancing humancomputer interaction by enabling chatbots to respond empathetically based on detectedemotions, with applications in mental health support, customer service, and virtual assistants.
Author: Dr.T.Amalraj Victoire, M.Vasuki, M.Prathis
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6950050590
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