用机器学习和Transformer模型分析印尼学生对AI教育的感知。
Sentiment Analysis of AI Adoption in Indonesian Higher Education Using Machine Learning and Transformer-Based Models
- 对比TF-IDF与Transformer方法分析学生意见
- DistilBERT达84.78%准确率，优于传统模型
- 适合关注AI教育舆情与NLP应用的研究者
本研究采用两种方法分析印尼学生对人工智能在高等教育中应用的看法：基于TF-IDF的机器学习与基于Transformer的深度学习。数据集包含2,295个标注样本，结合1,154条学生意见及额外词汇情感数据。评估了LightGBM、随机森林和支持向量机（SVM）等机器学习模型，同时对DistilBERT进行微调以实现二元情感分类。结果表明，SVM在机器学习模型中表现最佳，测试准确率达82.14%，F1得分为82.14%；而DistilBERT整体表现最优，准确率为84.78%，F1得分为84.75%。结果表明，基于Transformer的模型更能捕捉上下文信息，但SVM仍是高效且具有竞争力的替代方案。
原文摘要 · Abstract (English)
This study analyzes Indonesian student opinions on the adoption of artificial intelligence in higher education using two approaches: TF-IDF-based machine learning and Transformer-based deep learning. The dataset consists of 2,295 labeled samples, combining 1,154 student opinions with additional lexical sentiment data. LightGBM, Random Forest, and Support Vector Machine (SVM) are evaluated as machine learning models, while DistilBERT is fine-tuned for binary sentiment classification. The results show that SVM achieves the best performance among the machine learning models with 82.14% test accuracy and F1-score, while DistilBERT performs best overall with 84.78% accuracy and 84.75% F1-score. These findings indicate that Transformer-based models better capture contextual information, although SVM remains a competitive and efficient alternative for sentiment classification.
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