混合TF-IDF逻辑回归与MLP神经网络基线用于印尼语三类社交媒体文本情感分析
Hybrid TF--IDF Logistic Regression and MLP Neural Baseline for Indonesian Three-Class Sentiment Analysis on Social Media Text
AI总结：
本文研究了印尼语社交媒体文本的三类情感分析，结合TF-IDF文本特征、三种轻量级数字元数据特征和平衡多项式逻辑回归分类器，与基于MLP的神经网络基线进行比较，验证了预处理和特征工程在小数据集中的有效性。
摘要：
本文提出了一种紧凑的三类情感分析研究，用于印尼语社交媒体文本。任务通过细粒度情感数据集生成正、负和中性输出。所提出的实用基线结合了TF-IDF文本特征、三种轻量级数字元数据特征和平衡的多项式逻辑回归分类器。为了比较，研究还包含了一个使用两层多层感知机（MLP）的神经网络基线，基于相同的混合特征表示。数据集最初包含732行和191个细粒度情感标签；经过清洗、去重和标签重映射后，剩下707个样本，具有不平衡的分布：459个正样本、188个负样本和60个中性样本。实验结果表明，逻辑回归部署模型达到了0.8028的准确率、0.8003加权F1和0.7276宏F1，而项目文档报告了一个更高准确率但非生产性的MLP基线。这些发现表明，仔细的预处理、可解释的特征工程和类别平衡在小印尼语情感数据集中仍然具有竞争力，而神经网络基线应更好地视为比较实验而非默认部署模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
