SVM比LSTM更适合邮件情感分析，精度高且速度快。
A Comparison of Traditional Machine Learning Algorithms and LSTM-Based Deep Learning Models for Email Sentiment Analysis
- 用Word2Vec提取特征，比较SVM、逻辑回归等传统模型与LSTM。
- SVM线性核达到98.74%准确率，LSTM召回率高但耗时长。
- 适合需要快速精准过滤邮件的办公与学术场景。
电子通信的快速发展推动了邮件分类与情感检测系统的迫切需求。本研究对比了传统机器学习算法与深度学习架构在邮件情感分析中的表现，涵盖支持向量机（SVM）、逻辑回归、朴素贝叶斯及长短期记忆网络（LSTM）。采用Word2Vec嵌入进行特征表示，实验结果表明，使用线性核的SVM模型在效率与准确率上均表现最优，最高达到98.74%。尽管LSTM在识别垃圾邮件相关情感方面展现出优异的召回能力，但其计算时间远高于判别性统计模型。通过混淆矩阵的详细评估发现，传统分类器在密集向量空间中仍具高度鲁棒性。研究结论指出，对于邮件检测任务，SVM在预测精度与处理速度之间实现了最佳平衡。这些发现为专业与学术环境中构建高性能自动化邮件过滤系统提供了关键参考。
原文摘要 · Abstract (English)
The rapid growth of electronic communication has necessitated more robust systems for email classification and sentiment detection. This study presents a comparative performance analysis between traditional machine learning algorithms and deep learning architectures, specifically focusing on Support Vector Machines (SVMs), Logistic Regression, Naive Bayes, and Long Short-Term Memory (LSTM). Utilizing Word2Vec embeddings for feature representation, our experimental results indicate that the SVM model with a linear kernel achieves the highest efficiency and accuracy, reaching a peak performance of 98.74%. While the LSTM model demonstrates exceptional recall capabilities in detecting spam-related sentiments, it requires significantly more computational time compared to discriminative statistical models. Detailed evaluations via confusion matrices further reveal that traditional classifiers remain highly robust for dense vector spaces. This research concludes that for email detection tasks, SVM offers the most optimal balance between predictive precision and processing speed. These findings provide critical insights for developing high-performance automated email filtering systems in professional and academic environments.
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