Keyword based Hybrid Approach for Aspect based Sentiment Analysis of Course Feedback Data in Education
C.A. Sowndarya, Shashi Dahiya, Alka Arora, Anshu Bhardwaj, Mukesh Kumar, Mrinmoy Ray, V. Ramasubramanian, Anil Kumar · International Journal of Agricultural and Statistical Sciences · 2025
This study investigates aspect-based sentiment analysis of educational course feedback using a hybrid approach that combines keyword-based aspect extraction with traditional Machine Learning, Deep Learning, and transformer-based model predictions. The proposed methodology leverages the interpretability of keyword matching alongside the adaptability of Machine Learning, Deep Learning and transformer models to enhance overall performance, particularly when dealing with imbalanced, multi-aspect datasets like those derived from Massive Open Online Courses. The development of the hybrid approach involved training on a balanced synthetic dataset to establish a foundational understanding of aspect distribution, followed by fine-tuning on an imbalanced real-world dataset, thereby improving robustness in handling skewed data patterns. Aspect detection begins with a keyword-based extraction step, identifying relevant aspects within a review, which are then augmented with Machine Learning, Deep Learning and transformer model predictions to provide a more robust ensemble solution. Sentiment classification was applied to each detected aspect, focusing specifically on relevant segments of text to deliver more precise sentiment outcomes. This hybrid approach notably improved the interpretability and adaptability of traditional models. Transformer models such as Bidirectional Encoder Representations from Transformers and Robustly optimized Bidirectional Encoder Representations from Transformers approach achieved high precision, recall and F1-scores independently, demonstrating their capability in extracting complex, context-dependent sentiment without needing keyword support. Overall, the results highlight the benefits of integrating keyword-based and model-driven approaches for handling multi-aspect reviews, while also emphasizing the superior ability of transformer models to analyze nuanced feedback in educational settings.. KEYWORDS :Machine learning, Deep learning, Transformers, Aspect extraction, Sentiment analysis.