传统机器学习在微博情感分类中优于深度学习模型。
A Comparative Analysis of Machine Learning and Deep Learning Models for Tweet Sentiment Classification: A Case Study on the Sentiment140 Dataset
- 用TF-IDF特征+逻辑回归对比BiLSTM模型
- 逻辑回归准确率达73.5%，高于BiLSTM的69.17%
- 适合关注高效文本分类的开发者与研究者
社交媒体的爆发式增长催生了实时分析公众情绪的迫切需求。本研究在Sentiment140数据集的10,000条推文子集上，对比了基于TF-IDF特征的传统逻辑回归模型与深度学习的双向长短期记忆（BiLSTM）架构。实验结果表明，逻辑回归表现更优，准确率达到73.5%，高于BiLSTM的69.17%，且后者出现轻微过拟合。研究指出，对于中等规模的非正式文本数据，结合有效特征提取的经典机器学习方法可能优于复杂度更高的深度学习模型。最终，训练好的模型被集成到基于Streamlit的交互式网页应用中，并部署于Hugging Face Spaces供公众使用。
原文摘要 · Abstract (English)
The exponential growth of social media has created an urgent need for automated systems to analyze unstructured public sentiment in real time. This study compares a traditional Logistic Regression model using TF-IDF features with a deep learning Bidirectional Long Short-Term Memory (BiLSTM) architecture on a 10,000-tweet subset of the Sentiment140 dataset. Experimental results show that Logistic Regression outperformed BiLSTM, achieving an accuracy of 73.5% compared with 69.17%, while the deep learning model exhibited mild overfitting. These findings suggest that for medium-scale informal text data, classical machine learning with robust feature extraction can outperform more complex deep learning approaches. Finally, the trained models were integrated into an interactive web application using Streamlit and deployed on Hugging Face Spaces for public access.
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