期刊
ETRI JOURNAL
卷 43, 期 1, 页码 95-108出版社
WILEY
DOI: 10.4218/etrij.2019-0443
关键词
data mining; ensemble learning; Google app rating; opinion mining; text features; text mining
资金
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2019R1A2C1006159]
- MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program [IITP-2019-2016-0-00313]
This study aims to predict the numeric ratings of Google apps using machine learning classifiers, utilizing user reviews and TF/IDF features in an ensemble learning model. The research demonstrates the high potential for machine learning-based classifiers to accurately predict numeric ratings based on user reviews.
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.
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