A Novel Stacking Ensemble Learning Approach for Emotion Detection in Audio-to-Text Transcriptions

Shintami Chusnul Hidayati*, Muhammad Subhan, Yeni Anistyasari

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publication2024 International Seminar on Intelligent Technology and Its Applications
Subtitle of host publicationCollaborative Innovation: A Bridging from Academia to Industry towards Sustainable Strategic Partnership, ISITIA 2024 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages512-517
Number of pages6
Edition2024
ISBN (Electronic)9798350378573
DOIs
Publication statusPublished - 2024
Event25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024 - Hybrid, Mataram, Indonesia
Duration: 10 Jul 202412 Jul 2024

Conference

Conference25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024
Country/TerritoryIndonesia
CityHybrid, Mataram
Period10/07/2412/07/24

Keywords

  • audio-to-text transcription
  • emotion detection
  • natural language processing
  • quality education
  • stacking ensemble learning

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