A course review analysis using bidirectional long short-term memory model

Abstract

In recent years, sentiment analysis and online review analysis have gained popularity as critical components in the growth and development of educational courses. An innovative method has been created to increase the quality of learning experiences by rapidly collecting relevant data from course comments. This technique leverages bidirectional encoder representation from transformers (BERT) for word vector training. When combined with a learning mechanism, the recommended BERT accurately predicts the sentiment of online course reviews. Additionally, a dual-channel model based on Bi-directional long short-term memory (Bi-LSTM) is employed to improve sentiment data and semantics. Following data collection from the Coursera dataset, preprocessing approaches such as tokenization, stop words removal and sentence metric creation are applied to convert input data into word vectors and identify fundamental text units using text segmentation. The results demonstrate the proposed approach’s superiority over existing methods, offering an accuracy of 81.45%, recall of 94.9%, precision of 93.7%, and F-score of 93.7%.