A hybrid stacked ensemble learning framework for multilabel text emotion detection

Understanding emotions in text is an important part of sentiment analysis, especially in areas like mental health monitoring, customer feedback analysis, hate speech and disaster management. Unlike basic sentiment analysis that classifies text as positive or negative, real human emotions are complex and often overlapping, requiring multi-label classification to accurately capture multiple emotional states within a single input. While transformer-based models have improved performance, challenges persist particularly in low-resource languages and culturally diverse contexts due to the scarcity of annotated data and the difficulty in generalizing in different languages. This study proposes Hyb-Stack, a hybrid stacked ensemble framework that integrates simple stacking and cross-validation stacking, combining predictions from four transformer-base models (BERT, DistilBERT, RoBERTa, and mBERT) using a Random Forest meta-classifier to enhance classification accuracy, adaptability, and cross-lingual generalisation. Hyb-Stack was evaluated on three datasets: a high-resource English corpus (SemEval-2018 Task 1 E-c) and two low-resource corpora, the Bahasa Indonesia hate speech and HaEmoC_V1, a newly constructed Hausa-language emotion corpus developed to address the lack of annotated data for this language. Experimental results demonstrate that mBERT outperforms individual base models, achieving F1-scores of 82.28 (HaEmoC_V1), 85.33 (Bahasa Indonesia), and 88.90 (SemEval-2018 English). Also, the EM-9 ensemble set (BERT + DistilBERT + mBERT) improves performance, yielding F1-scores of 89.48, 88.19, and 90.67 on the respective datasets, which surpasses both individual models and conventional ensemble strategies such as averaging and weighted averaging. These findings highlight the effectiveness of combining multiple transformers with an optimized decision layer to advance multi-label emotion classification on diverse linguistic contexts.