Leveraging Emotional Intelligence Metrics and NLP-Driven Sentiment Analysis for Predictive Workplace Mental Health Monitoring
Emotion recognition from user-generated text is vital for mental health monitoring, organizational well-being, and social media analytics. We propose a multi-label classification framework leveraging a fine-tuned BERT model to detect overlapping emotional states - joy, sadness, anger, and fear in so...
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| Veröffentlicht in: | 2025 International Conference on Sensors and Related Networks (SENNET) Special Focus on Digital Healthcare(64220) S. 1 - 6 |
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| Sprache: | Englisch |
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IEEE
24.07.2025
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| Abstract | Emotion recognition from user-generated text is vital for mental health monitoring, organizational well-being, and social media analytics. We propose a multi-label classification framework leveraging a fine-tuned BERT model to detect overlapping emotional states - joy, sadness, anger, and fear in social media posts. We curated and annotated a Reddit dataset spanning eight emotions and employed transfer learning to adapt a pre-trained BERT encoder. The system architecture, defined via UML diagrams, comprises modules for tokenization, classification, and an inference workflow exposed through a Flask API. Training and validation were conducted with the Hugging Face Transformers library on GPU-accelerated Google Colab, yielding an average F 1 -score of 82% across all labels. To demonstrate real-world applicability, we integrated the proposed model into a React-based interface that provides real-time emotion score visualizations. Load testing under concurrent users confirmed robust scalability and responsiveness. Case studies in healthcare screening, HR analytics, and digital well-being illustrate the proposed work potential to enhance emotion-aware services. By bridging emotional intelligence metrics and advanced sentiment analysis, the proposed framework establishes a scalable, interpretable foundation for emotion recognition in organizational contexts. |
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| AbstractList | Emotion recognition from user-generated text is vital for mental health monitoring, organizational well-being, and social media analytics. We propose a multi-label classification framework leveraging a fine-tuned BERT model to detect overlapping emotional states - joy, sadness, anger, and fear in social media posts. We curated and annotated a Reddit dataset spanning eight emotions and employed transfer learning to adapt a pre-trained BERT encoder. The system architecture, defined via UML diagrams, comprises modules for tokenization, classification, and an inference workflow exposed through a Flask API. Training and validation were conducted with the Hugging Face Transformers library on GPU-accelerated Google Colab, yielding an average F 1 -score of 82% across all labels. To demonstrate real-world applicability, we integrated the proposed model into a React-based interface that provides real-time emotion score visualizations. Load testing under concurrent users confirmed robust scalability and responsiveness. Case studies in healthcare screening, HR analytics, and digital well-being illustrate the proposed work potential to enhance emotion-aware services. By bridging emotional intelligence metrics and advanced sentiment analysis, the proposed framework establishes a scalable, interpretable foundation for emotion recognition in organizational contexts. |
| Author | R, Dhinesh Kumar Jain, Pratham Vaid, Medha Shekhar, Kumar Animesh Bhadauriya, Rajvardhan Singh A, Rammohan |
| Author_xml | – sequence: 1 givenname: Rajvardhan Singh surname: Bhadauriya fullname: Bhadauriya, Rajvardhan Singh email: Rajvardhan.108005@stu.upes.ac.in organization: University of Petroleum and Energy Studies,School of Computer Science,Dehradun,India – sequence: 2 givenname: Kumar Animesh surname: Shekhar fullname: Shekhar, Kumar Animesh email: kumar.107110@stu.upes.ac.in organization: University of Petroleum and Energy Studies,School of Computer Science,Dehradun,India – sequence: 3 givenname: Pratham surname: Jain fullname: Jain, Pratham email: pratham.107064@stu.upes.ac.in organization: University of Petroleum and Energy Studies,School of Computer Science,Dehradun,India – sequence: 4 givenname: Medha surname: Vaid fullname: Vaid, Medha email: Medha.107848@stu.upes.ac.in organization: University of Petroleum and Energy Studies,School of Computer Science,Dehradun,India – sequence: 5 givenname: Dhinesh Kumar surname: R fullname: R, Dhinesh Kumar email: dhineshk.ravi@ddn.upes.ac.in organization: University of Petroleum and Energy Studies,School of Computer Science,Dehradun,India – sequence: 6 givenname: Rammohan surname: A fullname: A, Rammohan email: rammohan.a@vit.ac.in organization: Vellore Institute of Technology,Automotive Research Centre,Vellore,India |
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| SubjectTerms | BERT Bidirectional control Emotion Classification Emotion recognition Encoding Measurement Mental health Monitoring Multi-label Learning Real-time systems Sentiment analysis Social networking (online) UML Unified modeling language |
| Title | Leveraging Emotional Intelligence Metrics and NLP-Driven Sentiment Analysis for Predictive Workplace Mental Health Monitoring |
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