arXiv 2604.274392026-04-30cs.CL

基于机器学习和Transformer模型的印度尼西亚高等教育中人工智能采用的 sentiment 分析

Sentiment Analysis of AI Adoption in Indonesian Higher Education Using Machine Learning and Transformer-Based Models

Faculty of Science Sumatra Institute of Technology Lampung, Indonesia（苏门答腊技术学院理工大学科学学院，印度尼西亚朗格朗）

Happy Syahrul Ramadhan, Ahmad Sahidin Akbar, Karin Yehezkiel Sinaga, Luluk Muthoharoh, Ardika Satria, Martin C. T. Manullang

备注：: 8 pages, 6 figures, 7 tables. The paper compares TF-IDF-based machine learning models and DistilBERT for Indonesian sentiment analysis on student opinions about AI adoption in higher education. The manuscript reports that DistilBERT achieves the best overall test performance, while SVM is the strongest classical baseline

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AI总结：

本文比较了TF-IDF基于机器学习和Transformer模型在分析印尼学生对AI在高等教育中采用意见的sentiment效果，发现Transformer模型在准确率和F1分数上表现更优，而SVM在机器学习模型中表现最佳。

摘要：

本研究利用TF-IDF基于机器学习和Transformer基于深度学习两种方法，分析印尼学生对人工智能在高等教育中采用的sentiment。数据集包含2295个标注样本，结合1154个学生意见和额外的词性sentiment数据。LightGBM、随机森林和支持向量机（SVM）作为机器学习模型进行评估，而DistilBERT则进行二元sentiment分类的微调。结果表明，SVM在机器学习模型中表现最佳，测试准确率为82.14%和F1分数82.14%，而DistilBERT在整体表现上最佳，准确率为84.78%和F1分数84.75%。这些发现表明，基于Transformer的模型更能捕捉上下文信息，尽管SVM仍然是一个有竞争力且高效的sentiment分类替代方案。

英文摘要：

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.

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。