Open AccessPredicting numeric ratings for Google apps using text features and ensemble learning
Author(s) -
Umer Muhammad,
Ashraf Imran,
Mehmood Arif,
Ullah Saleem,
Choi Gyu Sang
Publication year - 2021
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2019-0443
Subject(s) - bigram , trigram , computer science , machine learning , artificial intelligence , classifier (uml) , tf–idf , ensemble learning , data mining , term (time) , physics , quantum mechanics
Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning‐based classifiers to predict authentic numeric ratings based on actual user reviews.