Suggestion Mining for Mobile APP Quality Improvement
Makarand Lotan Mali, Nitin N. Patil · 2024
User feedback is essential for mobile apps to continuously improve. However, obtaining insightful suggestions from user reviews still poses a major challenge. In order to address this issue, this study suggests a brand-new rule-based method for suggestion mining that focuses on the action verbs’ capacity to provide information. To detect user evaluations with suggestions, our approach makes use of verb semantics and pre-established criteria based on action verbs. We undertake a comprehensive performance evaluation on an actual dataset of mobile app evaluations to determine its efficacy. The outcomes demonstrate the substantial potential of our rule-based methodology, attaining superior memory and precision in identifying suggestions in reviews. Through the utilization of action verbs’ rich information, our approach provides a useful and effective way to automatically derive actionable suggestions from user reviews. We obtained above 89% accuracy for enhanced proposed method. In the end, this helps to improve the quality of mobile apps by enabling data-driven adjustments based on user reviews.