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Contradiction in text review and apps rating: prediction using textual features and transfer learning.
Aljrees, Turki; Umer, Muhammad; Saidani, Oumaima; Almuqren, Latifah; Ishaq, Abid; Alsubai, Shtwai; Eshmawi, Ala' Abdulmajid; Ashraf, Imran.
Afiliación
  • Aljrees T; College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin, Saudi Arabia.
  • Umer M; Department of Computer Science, Islamia University of Bahawalpur, Bahawalpur, Punjab, Pakistan.
  • Saidani O; Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
  • Almuqren L; Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
  • Ishaq A; Department of Computer Science, Islamia University of Bahawalpur, Bahawalpur, Punjab, Pakistan.
  • Alsubai S; Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.
  • Eshmawi AA; Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.
  • Ashraf I; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of Korea.
PeerJ Comput Sci ; 10: e1722, 2024.
Article en En | MEDLINE | ID: mdl-38196956
ABSTRACT
Mobile app stores, such as Google Play, have become famous platforms for practically all types of software and services for mobile phone users. Users may browse and download apps via app stores, which also help developers monitor their apps by allowing users to rate and review them. App reviews may contain the user's experience, bug details, requests for additional features, or a textual rating of the app. These ratings can be frequently biased due to inadequate votes. However, there are significant discrepancies between the numerical ratings and the user reviews. This study uses a transfer learning approach to predict the numerical ratings of Google apps. It benefits from user-provided numeric ratings of apps as the training data and provides authentic ratings of mobile apps by analyzing users' reviews. A transfer learning-based model ELMo is proposed for this purpose which is based on the word vector feature representation technique. The performance of the proposed model is compared with three other transfer learning and five machine learning models. The dataset is scrapped from the Google Play store which extracts the data from 14 different categories of apps. First, biased and unbiased user rating is segregated using TextBlob analysis to formulate the ground truth, and then classifiers prediction accuracy is evaluated. Results demonstrate that the ELMo classifier has a high potential to predict authentic numeric ratings with user actual reviews.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: PeerJ Comput Sci Año: 2024 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: PeerJ Comput Sci Año: 2024 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Estados Unidos