Aspect-based Sentiment Analysis with an Ensemble Learning Framework for Requirements Elicitation from App Reviews
Year: 2024
Type: article
Abstract: In the past three years, numerous studies have demonstrated the effective performance of aspect-based sentiment analysis (ABSA) in eliciting requirements from APP reviews. However, in aspect category detection (ACD) and aspect category polarity (ACP), traditional supervised machine learning techniques still dominate the latest research. Inherent limitations, such as poor generalization ability, low robustness, and high dependence on feature engineering, often constrain the methods. Additionally, while most research continues to center on the efficacy of singular models or strategies, the effective amalgamation of traditional and contemporary techniques has yet to be adequately explored. Given these challenges, this study proposes an ensemble learning framework based on XGBoost and Stacking. Compared to baseline, the framework achieves a performance improvement of 22.9%-28.4% in ACD and 9.3%-13.2% in ACP tasks based on different feature engineering. Overall, the preliminary attempt in this study to apply ensemble learning in ABSA for requirements elicitation from APP reviews, providing a feasible new direction for overcoming the inherent limitations of traditional techniques in fine-grained sentiment modeling in this field.
Source: 網際網路技術學刊
Authors: Zhiquan An, Teng Xiong, Zhiyuan Zou, Hongyan Wan
Institution: Wuhan Textile University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Award: G1078058450
OpenAlex ID: W4405702508