I am working on a sentiment analysis project where 90% of the reviews are positive, and my model is struggling to identify the negative ones correctly. How do you guys deal with such extreme class imbalance in text data? I’ve tried oversampling, but it seems to lead to overfitting. Are there specific loss functions or data augmentation techniques that work better for Natural Language Processing?
3 answers
Dealing with class imbalance in NLP requires a more nuanced approach than standard tabular data. Instead of simple oversampling, try using back-translation as a data augmentation technique. Translate your minority class (negative reviews) into another language and then back to English to create synthetic variations. Another powerful method is using a Weighted Cross-Entropy Loss function, which penalizes the model more heavily for misclassifying the minority class. This forces the neural network to pay more attention to the negative samples during training without the risk of just memorizing the exact same duplicated strings over and over.
Have you tried using Focal Loss? It’s often used in computer vision for imbalance but works surprisingly well for NLP tasks where some examples are much harder to classify than others.
Sometimes the best solution is to collect more data. If that’s not possible, try "Easy Data Augmentation" (EDA) which involves random synonym replacement.
Barbara is right; EDA is very effective. Replacing words with their WordNet synonyms can increase your minority class size while keeping the original sentiment intact.
David, I haven't tried Focal Loss yet, but that sounds like a great suggestion. Do you find that it requires a lot of hyperparameter tuning for the 'gamma' value to get it right? I’m currently using a standard BERT-base-uncased model, so I’ll need to see how easily I can swap out the loss function within the Hugging Face Trainer API for this specific experiment.