对比机器学习与BiLSTM在印尼Spotify评论情感分析中的表现
Sentiment Analysis of Indonesian Spotify Reviews Using Machine Learning and BiLSTM
- 用统一预处理流程比较经典模型与双层BiLSTM
- BiLSTM整体F1最高，但对中性类识别较差
- 数据增强后传统模型在三分类上更平衡
本文针对印尼Spotify评论进行三分类情感分析，对比了支持向量机、朴素贝叶斯和决策树等经典机器学习模型与两层双向长短期记忆网络（BiLSTM）的性能。基于10万条爬取的评论和70,155条清洗后的样本，所有方法采用相同的预处理流程，包括网络俚语规范化、停用词移除和词干提取。决策树在经典模型中表现最佳，而BiLSTM获得最高的加权F1分数，但在少数类（中性）上表现不佳。研究结论表明，尽管BiLSTM在整体情感识别上更强，但结合SMOTE的数据增强技术后，传统机器学习模型能实现更均衡的三分类性能。
原文摘要 · Abstract (English)
This paper benchmarks classical machine learning and deep learning approaches for three-class sentiment classification of Indonesian Spotify reviews. Using 100,000 scraped reviews and 70,155 cleaned samples, the study compares Support Vector Machine, Multinomial Naive Bayes, and Decision Tree models with a two-layer BiLSTM. Both approaches use the same preprocessing pipeline, including slang normalization, stopword removal, and stemming. Decision Tree achieves the best performance among the classical models, while BiLSTM attains the highest weighted F1-score overall but fails on the minority neutral class. The paper concludes that BiLSTM is stronger for overall sentiment detection, whereas machine learning with SMOTE provides more balanced three-class performance.
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