对比经典与深度学习在影评情感分类中的表现，发现传统方法更优。
A Comparative Analysis of Classical Machine Learning and Deep Learning Approaches for Sentiment Classification on IMDb Movie Reviews
- 用TF-IDF+AutoML筛选经典模型，对比BiLSTM与带注意力的BiLSTM
- SVM达85.3%准确率，超越深度模型；带注意力的BiLSTM提升至70.6%
- 适合资源有限时快速搭建高效情感分类系统
本文对IMDb影评数据集上的经典机器学习与深度学习方法进行对比研究。机器学习流程采用TF-IDF特征和PyCaret AutoML，评估逻辑回归、朴素贝叶斯和支持向量机；深度学习流程实现BiLSTM及带注意力机制的BiLSTM。实验结果显示，经典机器学习方法，尤其是支持向量机（SVM），以85.3%的准确率表现最佳，优于本研究中的深度学习模型。带注意力机制的BiLSTM相比标准BiLSTM有所提升，达到70.6%的准确率，表明其具备更强的上下文建模能力。论文结论指出，尽管深度学习能捕捉序列依赖关系，但在数据和算力受限的情况下，结合有效特征工程（如TF-IDF）的经典机器学习仍为强有力的基线方案。
原文摘要 · Abstract (English)
This paper presents a comparative study of classical machine learning and deep learning methods for sentiment classification on the IMDb movie reviews dataset. The machine learning pipeline uses TF-IDF features and PyCaret AutoML to evaluate Logistic Regression, Naïve Bayes, and Support Vector Machine, while the deep learning pipeline implements BiLSTM and BiLSTM with an attention mechanism. Experimental results show that classical machine learning, especially SVM, achieves the best performance with an accuracy of 0.8530, outperforming the deep learning models in this study. The BiLSTM with Attention model improves over the standard BiLSTM and reaches an accuracy of 0.706, indicating better contextual modeling. The paper concludes that although deep learning can capture sequential dependencies, classical machine learning remains a strong baseline when combined with effective feature engineering such as TF-IDF, particularly under limited data and computational resources.
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