用轻量级模型实现印尼社交文本三分类情感分析，效果优于神经网络。
Hybrid TF--IDF Logistic Regression and MLP Neural Baseline for Indonesian Three-Class Sentiment Analysis on Social Media Text
- 结合TF-IDF与元数据特征，用逻辑回归做分类。
- 准确率80.28%，加权F1达80.03%，中性类表现较弱。
- 适合资源有限、需可解释性的印尼语情感分析场景。
本文针对印尼社交文本开展小型三分类情感分析研究，输出包括正面、负面和中性三类，基于细粒度情绪数据集构建。提出一个实用基线模型，融合TF-IDF文本特征与三个轻量级数值元数据特征，并采用平衡的多项式逻辑回归分类器。为对比，还使用相同混合特征表示的两层多层感知机（MLP）作为神经基线。原始数据含732条记录与191个细粒度情绪标签；经清洗、去重及标签重映射后，剩余707个样本，分布为459个正面、188个负面、60个中性。实验显示，逻辑回归模型达到0.8028准确率、0.8003加权F1和0.7276宏观F1；项目文档报告更高性能但非生产可用的MLP基线。结果表明，精心预处理、可解释特征工程与类别平衡在小规模印尼语情感数据上仍具竞争力，而神经基线更适合作为对比实验而非部署首选。
原文摘要 · Abstract (English)
This paper presents a compact three-class sentiment analysis study for Indonesian social media text. The task is formulated with positive, negative, and neutral outputs derived from a fine-grained emotion dataset. The proposed practical baseline combines TF--IDF text features, three lightweight numeric metadata features, and a balanced multinomial Logistic Regression classifier. For comparison, the study also includes a neural baseline using a two-layer multilayer perceptron (MLP) over the same hybrid feature representation. The dataset originally contains 732 rows and 191 fine-grained emotion labels; after cleaning, deduplication, and label remapping, 707 samples remain with an imbalanced distribution of 459 positive, 188 negative, and 60 neutral instances. Experimental results show that the Logistic Regression deployment model reaches 0.8028 accuracy, 0.8003 weighted F1, and 0.7276 macro F1, while project documentation reports a higher-accuracy but non-production MLP baseline. These findings indicate that careful preprocessing, interpretable feature engineering, and class balancing remain competitive for small Indonesian sentiment datasets, whereas the neural baseline is better treated as a comparative experiment than as the default deployment model.
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