arXiv 2604.253922026-04-28cs.CL

在印尼IKN推文数据上，PyCaret AutoML与IndoBERT微调在情感分析中的基准测试

Benchmarking PyCaret AutoML Against IndoBERT Fine-Tuning for Sentiment Analysis on Indonesian IKN Twitter Data

Institut Teknologi Sumatera Lampung Selatan（印度尼西亚苏门答腊理工大学）

Mutia Alfi Mayzaroh, Dwi Fitria Ningsih, Nindi Destriani, Martin C. T. Manullang

备注：: 10 pages, 5 figures, 4 tables. Presented as a benchmarking study on Indonesian sentiment analysis using PyCaret and IndoBERT

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AI总结：

本文比较了PyCaret AutoML的传统机器学习方法与IndoBERT微调的深度学习方法，用于印尼语言推文的二分类情感分析。实验显示IndoBERT在准确率和F1分数上均优于传统模型。

摘要：

本文比较了基于PyCaret AutoML的传统机器学习方法与基于IndoBERT微调的深度学习方法，用于印尼语言推文的二分类情感分析。数据集包含1,472个手动标注样本，其中780个为负面评论，692个为正面评论。在机器学习设置中，Logistic Regression、Naive Bayes和支持向量机通过10折交叉验证评估，Logistic Regression在传统模型中表现最佳，准确率为77.57%，F1分数为77.17%。在深度学习设置中，indobenchmark/indobert-base-p1模型经过五轮微调，测试准确率为89.59%，F1分数为89.37%。结果表明，IndoBERT显著优于传统机器学习基线，突显了基于Transformer的上下文表示在非正式印尼社交媒体文本中的有效性。

英文摘要：

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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