Comparative Analysis of Machine Learning and Deep Learning Models for Aspect-based Sentiment Analysis in Education

Abstract

Aspect-Based Sentiment Analysis (ABSA) has emerged as a powerful technique for analyzing student feedback in educational settings, providing a deeper understanding of sentiments linked to specific aspects such as course content, instructor performance, assessment quality and technology support. Unlike traditional sentiment analysis, ABSA enables granular insights by extracting multiple aspects from a single review and assigning sentiments to each aspect independently. This study evaluates the performance of traditional Machine Learning (ML) models, including Logistic Regression (LR), Support Vector Machines (SVM), Naïve Bayes (NB), Random Forest (RF) and Gradient Boosting (GB), alongside advanced Deep Learning (DL) models such as Multi-Layer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT). The focus is on addressing the challenge of handling multiple aspects per review and performing aspect-specific sentiment classification. Experimental results demonstrate that BERT significantly outperforms other models in both tasks, offering superior precision, recall and F1-scores. Notably, BERT excels in handling complex, multi-aspect feedback, providing more accurate sentiment classification for each aspect. These findings highlight the importance of leveraging advanced models to analyze educational feedback effectively, enabling institutions to implement targeted improvements in key areas of learning and teaching.