对比自动机器学习与深度学习在20类情绪识别中的表现
Benchmarking PyCaret AutoML Against BiLSTM for Fine-Grained Emotion Classification: A Comparative Study on 20-Class Emotion Detection
- 用TF-IDF特征对比ML与DL模型，重点评估BiLSTM性能
- BiLSTM达89%准确率，略胜于最佳传统模型SVM的88.11%
- 适合关注情绪分析与模型效率平衡的研究者参考
细粒度情绪分类（如快乐、愤怒、悲伤、恐惧等）是自然语言处理中的难点。本研究基于包含79,595条英文句子的20-Emotion Text Classification Dataset，对经典机器学习与深度学习方法进行20类情绪分类对比。机器学习方面，使用TF-IDF特征评估逻辑回归、多项式朴素贝叶斯和支持向量机；深度学习方面，对比双向长短期记忆网络（BiLSTM）、门控循环单元（GRU）及轻量级PyTorch实现的Transformer。结果表明，BiLSTM表现最优，准确率达89%，加权F1-score为0.89，略优于最佳机器学习模型SVM的88.11%准确率。研究显示，尽管传统机器学习模型仍具竞争力且计算高效，但序列型深度学习模型更善于捕捉文本中的上下文情感线索。
原文摘要 · Abstract (English)
Fine-grained emotion classification, which identifies specific emotional states such as happiness, anger, sadness, and fear, remains a challenging task in natural language processing. This study benchmarks classical machine learning and deep learning approaches for 20-class emotion classification using the 20-Emotion Text Classification Dataset containing 79,595 English sentences. On the machine learning side, Logistic Regression, Multinomial Naive Bayes, and Support Vector Machine are evaluated using TF-IDF features. On the deep learning side, Bidirectional Long Short-Term Memory, Gated Recurrent Unit, and a lightweight Transformer implemented in PyTorch are compared. The results show that BiLSTM achieves the best overall performance with 89% accuracy and a weighted F1-score of 0.89, slightly outperforming the best machine learning model, SVM, which reaches 88.11% accuracy. The findings indicate that while traditional machine learning models remain competitive and computationally efficient, sequence-based deep learning models better capture contextual emotional cues in text.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。