Validation of Some Health Fitness Apps Using Users’ Reviews
Baale Abimbola Adebisi, Alade Ridwan Olamide, Adigun Arafat Adeniyi
Asian Journal of Research in Computer Science · pp. 13–21 · Published 30 May 2022
10.9734/ajrcos/2022/v14i130324Abstract
With the increase in the number of Health Fitness Applications (Apps) available for free, there is a growing concern as to whether these apps actually help individuals achieve personal fitness. This research developed a system to validate three Health Fitness Apps before user download using user reviews. Sentiment Analysis as the application of natural language processing, computational linguistics, and text analytics was used to identify and classify subjective opinions in the reviews of three most commonly used Health Fitness Applications; Samsung Health, Google Fit and Home Workout. Analysis showed that the Home Workout Fitness Application garnered a total of 99.9% Positive Reviews and can therefore be said to be the most effective of the three Apps considered, followed by Google Fit Fitness Application with a total of 37.4% Positive Reviews and Samsung Health Fitness Application recorded the most Negative Reviews of 96.6%.
References (14)
- 1 Sentiment analysis algorithms and applications: A survey [DOI]
- 2 A Survey of Opinion Mining and Sentiment Analysis [DOI]
- 3 Keep Using My Health Apps: Discover Users' Perception of Health and Fitness Apps with the UTAUT2 Model [DOI]
- 4 A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification [DOI]
- 5 Smartphone Applications for Patients' Health and Fitness [DOI]
- 6 Analysis of user comments: An approach for software requirements evolution [DOI]
- 7 Evaluation of a website-delivered computer-tailored intervention for increasing physical activity in the general population [DOI]
- 8 Internet-Based Physical Activity Interventions [DOI]
- 9 Using Mobile RE Tools to Give End-Users Their Own Voice [DOI]
- 10 The Intention to Use Fitness and Physical Activity Apps: A Systematic Review [DOI]
- 11 Self‐reported health, self‐reported fitness, and all‐cause mortality: Prospective cohort study [DOI]
- 12 Mining User Reviews for Mobile App Comparisons [DOI]
- 13 User’s Perspective about Mobile Fitness Applications [DOI]
- 14 2009 17th IEEE International Requirements Engineering Conference [DOI]
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