Deep Learning versus Traditional Classifiers on Vietnamese Students' Feedback Corpus

TL;DR

This paper compares deep learning and traditional machine learning classifiers for sentiment and topic analysis of Vietnamese student feedback, demonstrating that Bi-Directional LSTM achieves the highest accuracy.

Contribution

It introduces a Vietnamese student feedback corpus and evaluates multiple classifiers, highlighting the superior performance of Bi-Directional LSTM for sentiment and topic classification.

Findings

Bi-Directional LSTM achieved 92.0% F1-score on sentiment classification.

Bi-Directional LSTM achieved 89.6% F1-score on topic classification.

The developed application helps institutions analyze student opinions effectively.

Abstract

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.