A Comparative Analysis of Classical Machine Learning and Deep Learning Approaches for Sentiment Classification on IMDb Movie Reviews
A comparative benchmark on IMDb sentiment classification reveals that classical machine learning methods, particularly SVM with TF-IDF features, still outperform contemporary deep learning architectures on this canonical task. While BiLSTM with attention mechanisms showed incremental gains over vanilla BiLSTM (70.6% vs lower), the classical pipeline achieved 85.3% accuracy, challenging the assumption that neural approaches universally dominate NLP benchmarks. The finding underscores a persistent gap between deep learning's theoretical appeal and practical performance on well-defined classification problems, relevant to practitioners evaluating model selection tradeoffs.
MentionsIMDb · BiLSTM · Support Vector Machine · PyCaret · TF-IDF
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