Abstract
In the evolving landscape of Natural Language Processing (NLP), understanding and interpreting human emotions from text remains a critical challenge, especially when dealing with transcriptions of audio content. This study proposes a novel approach to emotion detection utilizing a cutting-edge stacking ensemble learning framework. This model is designed to address the common issues in emotion detection from audio-to-text transcriptions, including the loss of emotional nuance and context during transcription. By integrating multiple machine learning algorithms and advanced feature extraction methods, our model can handle the complexities and variabilities inherent in transcribed text. Rigorous testing and validation on public datasets reveal that this ensemble approach significantly outperforms conventional methods that rely on a single algorithm, establishing a new standard for emotion detection in NLP applications.
Original language | English |
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Title of host publication | 2024 International Seminar on Intelligent Technology and Its Applications |
Subtitle of host publication | Collaborative Innovation: A Bridging from Academia to Industry towards Sustainable Strategic Partnership, ISITIA 2024 - Proceeding |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 512-517 |
Number of pages | 6 |
Edition | 2024 |
ISBN (Electronic) | 9798350378573 |
DOIs | |
Publication status | Published - 2024 |
Event | 25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024 - Hybrid, Mataram, Indonesia Duration: 10 Jul 2024 → 12 Jul 2024 |
Conference
Conference | 25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024 |
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Country/Territory | Indonesia |
City | Hybrid, Mataram |
Period | 10/07/24 → 12/07/24 |
Keywords
- audio-to-text transcription
- emotion detection
- natural language processing
- quality education
- stacking ensemble learning