对比机器学习与深度学习在印尼语反网络欺凌检测中的效果，发现逻辑回归和带注意力的BiLSTM表现最佳。
Comparative Analysis of AutoML and BiLSTM Models for Cyberbullying Detection on Indonesian Instagram Comments
- 采用领域定制预处理，包括俚语标准化、停用词删除和词干提取
- 逻辑回归在传统模型中表现最优，带注意力的BiLSTM在深度模型中最强
- 适合资源有限场景的高效模型选择，尤其关注印尼语非正式文本
本研究对比了机器学习与深度学习方法在印尼语Instagram评论中网络欺凌检测的效果。基于650条标注为欺凌与非欺凌的平衡数据集，评估了使用TF-IDF特征的朴素贝叶斯、逻辑回归和支持向量机，以及双向LSTM（BiLSTM）和带Bahdanau注意力的BiLSTM。针对非正式印尼语设计了预处理流程，包括俚语标准化、停用词移除和词干化。结果表明，逻辑回归在传统模型中表现最佳，而带注意力的BiLSTM在深度学习模型中取得最强性能。研究强调了领域特定预处理的重要性，显示尽管深度学习能更好捕捉上下文模式，但机器学习在资源受限部署中仍具竞争力。
原文摘要 · Abstract (English)
This study compares machine learning and deep learning approaches for cyberbullying detection in Indonesian-language Instagram comments. Using a balanced dataset of 650 comments labeled as Bullying and Non-Bullying, the study evaluates Naive Bayes, Logistic Regression, and Support Vector Machine with TF-IDF features, as well as BiLSTM and BiLSTM with Bahdanau Attention. A preprocessing pipeline tailored to informal Indonesian text is applied, including slang normalization, stopword removal, and stemming. The results show that Logistic Regression performs best among the machine learning models, while BiLSTM with Attention achieves the strongest overall deep learning performance. The findings highlight the value of domain-specific preprocessing and show that although deep learning captures contextual patterns more effectively, machine learning remains a competitive option for resource-constrained deployments.
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