Deep Learning versus Traditional Classifiers on Vietnamese Students' Feedback Corpus
| Autor: | Nguyen, Phu X. V., Hong, Tham T. T., Van Nguyen, Kiet, Nguyen, Ngan Luu-Thuy |
|---|---|
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | 5th NAFOSTED Conference on Information and Computer Science (NICS 2018) |
| Druh dokumentu: | Working Paper |
| Popis: | Student's feedback is an important source of collecting students' opinions to improve the quality of training activities. Implementing sentiment analysis into student feedback data, we can determine sentiments polarities which express all problems in the institution since changes necessary will be applied to improve the quality of teaching and learning. This study focused on machine learning and natural language processing techniques (NaiveBayes, Maximum Entropy, Long Short-Term Memory, Bi-Directional Long Short-Term Memory) on the VietnameseStudents' Feedback Corpus collected from a university. The final results were compared and evaluated to find the most effective model based on different evaluation criteria. The experimental results show that the Bi-Directional LongShort-Term Memory algorithm outperformed than three other algorithms in terms of the F1-score measurement with 92.0% on the sentiment classification task and 89.6% on the topic classification task. In addition, we developed a sentiment analysis application analyzing student feedback. The application will help the institution to recognize students' opinions about a problem and identify shortcomings that still exist. With the use of this application, the institution can propose an appropriate method to improve the quality of training activities in the future. Comment: In Proceeding of the 5th NAFOSTED Conference on Information and Computer Science (NICS 2018) |
| Databáze: | arXiv |
| Externí odkaz: |
|