Mobile Applications Rating Performance: A Survey


  • Sabreen Abulhaija Princess Sumaya University for Technology
  • Shayma Hattab
  • Ahmad Abdeen
  • Wael Etaiwi



Machine Learning, Mobile Applications, Rating Performance, Sentiment Analysis, Predictive Modeling


The use of mobile phones is increasing all the time. These phones have become increasingly vital and beneficial in all parts of our lives, including social and business sides. Mobile applications are expanding with new upgrades and editions every day due to this expansion. This increase makes it more difficult for consumers, particularly those who are not technologically minded, to determine which applications to install and use. It is much more difficult for developers to ensure that their apps will be used and lucrative. Several research papers have been published in the recent five years to investigate mobile applications' rating to aid users and developers in making the best decision possible by employing various classifications and methodologies. This study provides a literature review research analyzed mobile app evaluations from 2018 to 2022 using various datasets.  In addition, a new taxonomy is proposed to classify the research papers that looked at the rating of mobile apps into three categories: predictive modeling, sentiment analysis, and priority ranking of the most significant features




How to Cite

Abulhaija, S., Hattab, S. ., Abdeen, A., & Etaiwi, W. . (2022). Mobile Applications Rating Performance: A Survey. International Journal of Interactive Mobile Technologies (iJIM), 16(19), pp. 133–146.