Paper Title
COMPARISON OF DIFFERENT MODELS TO EVALUATE SENTIMENT AND EMOTIONS WELL BEING
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Registration ID: IJNRD_324703
Published ID: IJNRD2605269
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Keywords
“sentiment analysis”, “emotion recognition”, “well-being inference”, “conversational AI”, “deep learning”, “natural language processing, “BiLSTM”, “TextCNN”, “transformer models”
Abstract
Much of the human exchange now occurs through modern digital modes – largely replacing long-established channels that rely on paralinguistic and non-verbal expression in favour of text-based means where absent. While the abundance of written human expression has no precedent, reliably parsing text to assess affective state is still a largely unsolved problem — creating both a challenge and opportunity for computational analysis. The second introduces and assesses within a common multi-level sequential framework, several approaches to emotion recognition together with sentiment analysis and wellbeing inference into a unified processing pipeline. It first classifies the incoming text into one of seven discrete emotion categories, while also filtering low-confidence (50%) predictions related to emotions through a confidence-based mechanism before obtaining a sentiment label conditioned on the emotion and rule. Methods To examine the four representation models, three model architectures were trained and tested on three progressively more-complex datasets: Sentiment140 (binary tweet classification), GoEmotions (27-category Reddit comment classification) and MELD (multi-speaker conversational emotion recognition from TV dialogue). The results show that for binary classification tasks all the architectures achieve decent performance, with accuracy settling close to 81%. For fine-grained emotion recognition, the performance drops sharply to 50–53% on GoEmotions and even down to 15–28% on MELD. These failures can be analysed and found to result not from insufficiency of architecture but from context, as conversational emotion recognition cannot reliably occur on isolated utterances. These conclusions drive the essential design choices of the framework suggested, especially with regard to confidence filtering and separating out emotion, sentiment, and well-being stages as separate architectures designed for a specific data context. Ethical considerations of transparency, bias auditing and limiting automated inference to the level of groups rather than individuals are included as fundamental aspects of design instead of secondary or supplementary issues.
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How To Cite (APA)
Dr. Supreet Kaur Sahi, Prof (Dr.) Vandana Kalra, & Amaanya Kaur Dhall (May-2026). COMPARISON OF DIFFERENT MODELS TO EVALUATE SENTIMENT AND EMOTIONS WELL BEING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(5), c605-c619. https://ijnrd.org/papers/IJNRD2605269.pdf
Issue
Volume 11 Issue 5, May-2026
Pages : c605-c619
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Paper Reg. ID: IJNRD_324703
Published Paper Id: IJNRD2605269
Research Area: Other area not in list
Author Type: Indian Author
Country: New Delhi, Delhi, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2605269.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2605269
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