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Evaluating mobile app performance through sentiment analysis with SimCLR and MobileBERT.

Ruping Zhang1, Aman Ullah2, Khair Ullah Khan3

  • 1Department of Mathematics and Information Engineering, Liaocheng University, Dongchang College, Liaocheng, Shandong, China.

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Summary
This summary is machine-generated.

This study introduces a sentiment analysis (SA) model using SimCLR and MobileBERT for mobile app reviews. The model achieves high accuracy, offering developers nuanced insights into user feedback for app improvement.

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Area of Science:

  • Natural Language Processing (NLP)
  • Machine Learning
  • Artificial Intelligence (AI)

Background:

  • Mobile apps offer convenience but require effective user feedback analysis.
  • Traditional sentiment analysis models lack nuance, often limited to positive/negative classifications.
  • Appraisal theory provides a framework for deeper understanding of user sentiment beyond simple polarity.

Purpose of the Study:

  • To develop a sophisticated sentiment analysis (SA) model for mobile app reviews.
  • To measure perceived mobile app performance using nuanced sentiment categories.
  • To integrate appraisal theory with advanced machine learning for comprehensive user feedback analysis.

Main Methods:

  • A novel SA model combining SimCLR (Simple Contrastive Learning of Representations) for feature extraction and MobileBERT for classification.
  • Manual labeling of app reviews based on performance parameters aligned with appraisal theory's appreciation and judgment components.
  • Implementation of Explainable AI (XAI) using SHAP (SHapley Additive exPlanations) for annotation accuracy.
  • Incorporation of self-supervised learning (SimCLR), knowledge distillation, data augmentation (WordNet), PCA for dimensionality reduction, Optuna for hyperparameter optimization, and K-fold cross-validation.

Main Results:

  • The proposed model achieved a mean fold accuracy of 88.63% and ROC AUC of 90.91%.
  • The integration of SimCLR and MobileBERT, alongside optimization techniques, resulted in a highly efficient and accurate SA system.
  • The model demonstrates scalability and suitability for real-time sentiment analysis in resource-constrained environments.

Conclusions:

  • The developed SA model offers a significant advancement over traditional methods by providing deeper, nuanced insights into user sentiment.
  • This approach empowers developers to identify specific areas for app performance improvement.
  • The model serves as a valuable tool for stakeholders including marketers, product managers, and UX researchers, enhancing understanding of user feedback.