  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.
