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arXiv Machine Learning By Mahmoud S. Mayaleh, Samer A. Mayaleh

Enhancing Sentiment Classification on Small Datasets through Data Augmentation and Transfer Learning

Read the original on arXiv Machine Learning →

The paper investigates how data augmentation can improve sentiment classification on small datasets. Three augmentation techniques—Easy Data Augmentation (EDA), back‑translation, and contextual token substitution—are evaluated with both traditional classifiers (Logistic Regression, Random Forest) and BERT. Results show that all methods boost performance, with contextual augmentation best for BERT and EDA/back‑translation benefiting traditional models.

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