Automatic user sentiments extraction from diabetes mobile apps – An evaluation of reviews with machine learning

ABSTRACT

Using diabetes mobile apps for self-management of diabetes is one of the emerging strategies for controlling blood sugar levels and maintaining the wellness of patients with diabetes. This study aims to develop a strategy for thematically extracting user comments from diabetes mobile apps to understand the concern of patients with diabetes. Hence, 2678 user comments obtained from the Google Play Store are thematically analyzed with Non-negative Matrix Factorization (NMF) to identify the themes for describing positive, neutral, and negative sentiments. These themes are used as the ground truth for developing a 10-fold cross-validation ensemble Multilayer Artificial Neural Network (ANN) model following the Bag of Word (BOW) analysis of lemmatized user comments. The result shows that a total of 41.24% of positive sentimental users identified the diabetes mobile apps as Effective for Blood Sugar Monitoring (EBSM), 32.36% with neutral sentiments are mostly impressed by the Information Quality (IQ), whereas 40.81% of unhappy users are worried about the Poor Information Quality (PIQ). The prediction accuracy of the ANN model is 89%–97%, which is 5%–48% better than other predominant algorithms. It can be concluded from this study that diabetes mobile apps with a simple user interface, effective data storage and security, medication adherence, and doctor appointment scheduling are preferred by patients with diabetes.

KEYWORDS:

• Artificial neural network
• bag of words
• diabetes mobile apps
• natural language processing
• non-negative matrix factorization
• user review comments

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Funding

The author(s) reported there is no funding associated with the work featured in this article.