On Students’ Behavior Prediction for Library Service Quality Using Bidirectional Deep Machine Learning

Library service quality has been taken into account after the COVID-19 pandemic to propose appropriate conditions under the rapid change of technology circumstances. The main study in this paper is to consider the evaluation of students’ sentiments to understand the role of library service quality after the pandemic outbreak and evaluate library service quality consistent with the new situation. For this study, we employ deep learning models such as Convolutional Bidirectional Long Short-Term Memory (Conv-BiLSTM) and Convolutional Bidirectional Gated Recurrent Unit (Conv-BiGRU), Attention and Transformer TFBERT model. Our findings indicate that the Conv-BiLSTM (94.59%) and Conv-BiGRU (94.33%) outperformed the others, achieving the highest accuracy for the prediction of Vietnamese students’ sentiments about library service quality.