Identifying and resolving conflict in mobile application features through contradictory feedback analysis

As mobile applications proliferate and user feedback becomes abundant, the task of identifying and resolving conflicts among application features is crucial for delivering satisfactory user experiences. This research, motivated to align application development with user preferences, introduces a novel methodology that leverages advanced Natural Language Processing techniques. The paper showcases the use of sentiment analysis using RoBERTa, topic modeling with Non-negative matrix factorization (NMF), and semantic similarity measures from Sentence-BERT. These techniques enable the identification of contradictory sentiments, the discovery of latent topics representing application features, and the clustering of related feedback instances. The approach detects conflicts by analyzing sentiment distributions within semantically similar clusters, further enhanced by incorporating antonym detection and negation handling. It employs majority voting, weighted ranking based on rating scores, and frequency analysis of feature mentions to resolve conflicts, providing actionable insights for prioritizing requirements. Comprehensive evaluations on large-scale iOS App Store and Google Play Store datasets demonstrate the approach's effectiveness, outperforming baseline methods and existing techniques. The research improves mobile application development and user experiences by aligning features with user preferences and providing interpretable conflict resolution strategies, thereby introducing a novel approach to the field of mobile application development.