Enhancing sentiment classification on small datasets through data augmentation and transfer learning

Abstract Small-scale sentiment classification often suffers from data scarcity, which limits the generalization ability of the models. This study evaluates and compares the effectiveness of three data augmentation strategies: Easy Data Augmentation (EDA), back-translation, and contextual token substitution (nlpaug-style), with both traditional machine learning classifiers (Logistic Regression, Random Forest) and transformer-based models (BERT). We perform a comprehensive empirical comparison with low-resource sentiment datasets by summarizing the results of recent studies and performing targeted head-to-head experiments. Our findings indicate that all augmentation methods improve performance. Contextual augmentation yields the most consistent gains for BERT models, while EDA and back-translation provide greater benefits for traditional classifiers. These insights help guide the selection of data augmentation techniques tailored to model type and dataset size, filling a critical gap in research on data augmentation for sentiment classification on small datasets.

# Computational linguistics. Natural language processing # Electronic computers. Computer science # Sentiment analysis # Data augmentation # Transfer learning # Small datasets # BERT # Natural language processing

Mahmoud S. Mayaleh, Samer A. Mayaleh (2026). "Enhancing sentiment classification on small datasets through data augmentation and transfer learning". Discover Artificial Intelligence. https://doi.org/10.1007/s44163-025-00813-9