Clustering-Based Undersampling Strategies for Enhancing Classifier Performance in Imbalanced Text Datasets

Class imbalance significantly hinders sentiment classification, as minority classes – despite their semantic importance – are underrepresented during training. Traditional undersampling methods often rely on random removal or local heuristics, which can eliminate informative samples and disrupt the data’s global structure. To overcome these limitations, this study introduces two clustering-based undersampling methods, ClusUS and ClusUSCosine, designed to reduce redundancy in the majority class while preserving representative and meaningful instances. These approaches leverage structural and similarity-based selection to improve decision boundaries and enhance minority-class recognition. The proposed methods are evaluated on five Arabic and English benchmark datasets using two feature representations (TF-IDF and AraGPT2) and five deep learning models (CNN, LSTM, BiLSTM, RNN, and RNN-LSTM). Performance is measured using G-Mean, Macro F1, PR-AUC, and ranking-based stability analysis. Results show that traditional undersampling techniques are highly sensitive to feature representation, whereas ClusUS and ClusUSCosine deliver more consistent improvements. ClusUSCosine often achieves the best G-Mean, indicating strong minority-class performance, while ClusUS remains stable across settings. The study also highlights discrepancies between PR-AUC and balanced metrics, suggesting that ranking alone may overestimate performance. Overall, the proposed methods provide a robust, similarity-aware solution for imbalanced sentiment classification.