Sentiment analysis of student’s comments using long short-term memory with multi head attention

Abstract

Classroom teaching is a viable and effective approach for enhancing student learning and promoting engagement in the educational process. The opinions of students play a vital role in the evaluation of teachers. This paper presents a comprehensive overview of sentiment analysis techniques based on recent research and subsequently explores machine learning, i.e., ensemble classifiers, deep learning, long short-term memory (LSTM), convolutional neural network (CNN), LSTM with single attention, LSTM with multi-head attention, and feature extraction techniques (TFidfVector and Word2Vec), in the context of sentiment analysis over student opinion datasets, i.e., the Vietnamese student feedback corpus, as well as data collected from a final-year student's comment in 2023. Further, the Vietnamese student feedback corpus is translated to English and pre-processed with the proposed framework, which yields interesting facts about the capabilities and deficiencies of different methods. In this paper, we conducted experiments with ensemble classifiers, LSTM and CNN, LSTM with single attention, and LSTM with multi-head attention. We conclude that LSTM with multi-head attention produces an accuracy result of 95.57%, which outperform as compare to other three baseline methods and earlier studies.