Contradiction in text review and apps rating: prediction using textual features and transfer learning

Turki Aljrees; Muhammad Umer; Oumaima Saidani; Latifah Almuqren; Abid Ishaq; Shtwai Alsubai; Ala' Abdulmajid Eshmawi; Imran Ashraf

doi:10.7717/peerj-cs.1722

Contradiction in text review and apps rating: prediction using textual features and transfer learning

PeerJ Comput Sci. 2024 Jan 3:10:e1722. doi: 10.7717/peerj-cs.1722. eCollection 2024.

Authors

Turki Aljrees¹, Muhammad Umer², Oumaima Saidani³, Latifah Almuqren³, Abid Ishaq², Shtwai Alsubai⁴, Ala' Abdulmajid Eshmawi⁵, Imran Ashraf⁶

Affiliations

¹ College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin, Saudi Arabia.
² Department of Computer Science, Islamia University of Bahawalpur, Bahawalpur, Punjab, Pakistan.
³ Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
⁴ Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.
⁵ Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.
⁶ Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of Korea.

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.

Keywords: Ensemble learning; Google apps rating; Opinion mining; Text mining; Transfer learning.

Grants and funding

This article was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R349), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.