用IndoBERT微调比传统机器学习更准，适合处理印尼语社交媒体情感分析。
Benchmarking PyCaret AutoML Against IndoBERT Fine-Tuning for Sentiment Analysis on Indonesian IKN Twitter Data
- 对比PyCaret自动机器学习与IndoBERT微调方法
- IndoBERT达89.59%准确率，远超传统模型的77.57%
- 适合做印尼语社交媒体文本情感分析的研究者
本文对比基于PyCaret AutoML的经典机器学习方法与基于IndoBERT微调的深度学习方法，在1,472条人工标注的印尼语伊科恩（IKN）推文评论上进行二分类情感分析。数据集含780条负面评论和692条正面评论。机器学习设置中，逻辑回归、朴素贝叶斯和支持向量机经10折交叉验证，逻辑回归表现最佳，准确率为77.57%，F1得分为77.17%。深度学习设置中，使用indobenchmark/indobert-base-p1模型微调五轮，测试准确率达到89.59%，F1得分为89.37%。结果表明，基于Transformer的上下文表示在非正式印尼语社交媒体文本中具有显著优势。
原文摘要 · Abstract (English)
This paper benchmarks a classical machine learning approach based on PyCaret AutoML against a deep learning approach based on IndoBERT fine-tuning for binary sentiment analysis of Indonesian-language Twitter comments related to Ibu Kota Nusantara (IKN). The dataset contains 1,472 manually labeled samples, consisting of 780 negative and 692 positive comments. In the machine learning setting, Logistic Regression, Naive Bayes, and Support Vector Machine were evaluated using 10-fold cross-validation, with Logistic Regression achieving the best performance among the classical models at 77.57% accuracy and 77.17% F1-score. In the deep learning setting, the indobenchmark/indobert-base-p1 model was fine-tuned for five epochs and achieved 89.59% test accuracy and 89.37% F1-score. The results show that IndoBERT substantially outperforms the machine learning baselines, highlighting the effectiveness of Transformer-based contextual representations for informal Indonesian social media text.
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