When working on sentiment analysis with the IMDb movie review dataset, practitioners often hit hidden pitfalls that degrade model trustworthiness. First, the raw splits are already ordered by label, so any naïve subsample will create a severe class imbalance; always shuffle the data before selecting a subset. Second, exact duplicate reviews exist across train and test and even within the training set, causing leakage that inflates performance; compute MD5 hashes of the text to detect and drop duplicates. Third, review lengths vary widely—many exceed the typical token limit—so inspect the distribution (median, 75th, 95th percentiles) and decide whether to increase MAX_LEN, apply head‑tail truncation, or use a model with a longer context window.
Start with a strong, interpretable baseline: TF‑IDF (1‑2 grams, sublinear TF) paired with Logistic Regression. This gives you a quick accuracy and AUC reference and highlights the most predictive n‑grams for error analysis.
If you need higher performance, fine‑tune DistilBERT with LoRA adapters (r=16, α=32) using the Hugging Face Trainer, dynamic padding, early stopping, and mixed‑precision. Evaluate beyond accuracy: macro‑F1, ROC‑AUC, confusion matrices, and calibration curves. Use Expected Calibration Error and reliability plots to see if predicted probabilities match observed frequencies; adjust the decision threshold via a sweep if the default 0.5 is sub‑optimal.
Analyze mistakes: look at confident errors, group predictions by review length to spot truncation‑related drops, and apply occlusion saliency to see which words drive each prediction. For long reviews, compare head‑only versus tail‑only predictions to determine whether sentiment resides at the beginning, end, or both, and adapt your truncation strategy accordingly.
Finally, leverage the large unsupervised split: generate high‑confidence pseudo‑labels (prob > 0.95 or < 0.05) from your transformer, add them to the TF‑IDF training set, and re‑train. Verify any gain on a clean held‑out set to avoid amplifying teacher biases. Save the merged model and tokenizer for reusable inference.#AI #MachineLearning #NLP #SentimentAnalysis #DeepLearning #DataScience