A Dual-Phase Hyperparameter Tuning Approach for Emotion Detection Using Boosting-Based Machine Learning Algorithms

The purpose of this study is to develop and evaluate a dual-phase hyperparameter tuning approach for enhancing the performance of emotion detection systems using boosting-based machine learning algorithms. The methodology involves data collection and preprocessing, feature engineering, model definition, training, and evaluation. Specifically, the study applies a two-stage optimization process in initial coarse tuning with RandomizedSearchCV followed by fine-tuning with GridSearchCV on models including XGBoost, LightGBM, CatBoost and GradientBoosting. The results showed that LightGBM achieved the highest overall accuracy of 92.20%, followed by XGBoost with 91.47%, GradientBoosting with 91.19%, and CatBoost with 88.23%. Confusion matrix analysis revealed that LightGBM and XGBoost produced more balanced and accurate classifications across the six emotion classes, while CatBoost exhibited higher misclassification rates in challenging classes. In terms of computational efficiency, LightGBM provided the best balance between accuracy and training speed, whereas XGBoost demonstrated the lowest memory usage. GradientBoosting achieved competitive performance but required significantly higher computational resources, while CatBoost achieved the fastest prediction time. Based on the findings, LightGBM was identified as the most suitable boosting algorithm for emotion classification due to its superior balance of predictive performance, efficiency, and reliability. Future studies are recommended to explore hybrid and deep learning approaches, larger datasets, and real-time implementation strategies to further improve emotion classification systems.