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Sentiment Analysis Implementing BERT-based Pre-trained Language Model for Vietnamese

机译：情绪分析实施基于BERT的越南语预训练的语言模型

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Continuous Improvement Process Model contributes effectively to the educational development of any school. Sentiment analysis of student feedback is a step in this model to find suitable solutions to enhance the performance of instructors and the quality of material facilities. However, most of the state-of-the-art sentiment classification models only focus on English, by which some disadvantages in Vietnamese researches are brought on. We study a sentiment analysis model using PhoBERT pre-trained model for Vietnamese, which is a robust optimization for Vietnamese of the prominent BERT model. We then employ alternative fine-tuning techniques to generalize the model for multi-class classification other than the binary task. Our method achieves state-of-the-art results on the UIT-VSFC dataset with an F1-score of 93.92% and an accuracy of 94.28%. This is expected to be helpful for the improvement of Vietnam's education and set the foundation for researching in Vietnamese which is the language that lacks resources.
机译：持续改进过程模型有效地贡献了任何学校的教育发展。学生反馈的情感分析是该模型的一步，找到适当的解决方案，以提高教师的性能和材料设施的质量。然而，大多数最先进的情绪分类模型只关注英语，通过越南研究中的一些缺点被带到了。我们使用Phobert预先训练模型来研究一种情感分析模型，越南语是一种突出伯特模型的越南的鲁棒优化。然后，我们采用替代的微调技术来概括除了二进制任务之外的多级分类模型。我们的方法在UIT-VSFC数据集上实现了最先进的结果，F1分数为93.92％，精度为94.28％。这有望有助于改善越南教育，并为越南语进行研究基础，这是缺乏资源的语言。

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