Hybrid Deep Learning Approach for Automating App Review Classification: Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

Abstract

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products. Automating the analysis of these reviews is vital for efficient review management. While traditional machine learning (ML) models rely on basic word-based feature extraction, deep learning (DL) methods, enhanced with advanced word embeddings, have shown superior performance. This research introduces a novel aspect-based sentiment analysis (ABSA) framework to classify app reviews based on key non-functional requirements, focusing on usability factors: effectiveness, efficiency, and satisfaction. We propose a hybrid DL model, combining BERT (Bidirectional Encoder Representations from Transformers) with BiLSTM (Bidirectional Long Short-Term Memory) and CNN (Convolutional Neural Networks) layers, to enhance classification accuracy. Comparative analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance, with precision, recall, F1-score, and accuracy of 96%, 87%, 91%, and 94%, respectively. The significant contributions of this work include a refined ABSA-based relabeling framework, the development of a high-performance classifier, and the comprehensive relabeling of the Instagram App Reviews dataset. These advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.