Year Report 2023 (research in the energy sector)
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Publication Type
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Supervisor
Publication Year
2024
Journal
Book
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Publication Issue
Publication Begin page
Publication End page
Publication Number of pages
7
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Abstract
The 2023 Year Report for the PhD project between an energy business partner and Vlerick Business School highlights the research activities executed in 2023. The year began with an in-depth examination of inconsistencies in emotion labeling across multiple datasets, leading to the exploration of external knowledge-based frameworks for dataset enrichment. Machine learning models were assessed for their ability to generalize across datasets, highlighting the need for standardization in emotion classification.
Mid-year, the research shifted toward predictive modeling, integrating service interaction data with customer feedback to explore customer experience metrics. A cloud-based machine learning infrastructure was implemented to improve scalability, and transfer learning approaches were tested to enhance model robustness. Later in the year, comparative studies were conducted to evaluate different emotion classification methodologies and their applicability across diverse datasets. The findings underscored key challenges in emotion recognition and provided insights into improving predictive accuracy. Future research will refine these approaches and expand the analysis of customer interactions in service environments.
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Keywords
Energy, Datasets