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Unsupervised Machine Learning to Identify Positive and Negative Themes in Jordanian mHealth Apps

Author

Listed:
  • Mohammad Salem Alhur

    (Universidade de Santiago de Compostela, Spain & University of Jordan, Jordan)

  • Shaher Alshamari

    (Primary Health Care Corporation, Qatar)

  • Judit Oláh

    (John von Neumann University, Hungary & University of Johannesburg, South Africa)

  • Hanadi Aldreabi

    (University of Jordan, Jordan)

Abstract

User opinions are crucial in the development of mobile health (mHealth) applications (apps). This study aimed to investigate and qualitatively assess consumer attitudes toward mHealth apps and the main aspects of their design. The methodology was divided into four steps: (1) data collection, (2) preprocessing, (3) sentiment analysis by valence-aware dictionary and sentiment reasoner (VADER), and (4) thematic analysis by the latent Dirichlet allocation (LDA) algorithm. These steps were implemented in 836 reviews of eight mHealth apps on app stores in Jordan. The current study offers healthcare stakeholders insight into the positive and negative aspects of mHealth apps by identifying user-preferred features and recommending improvements. The findings indicate several aspects of design that mHealth app developers may use to improve overall efficacy, including user experience, client services, usability, and adherence.

Suggested Citation

  • Mohammad Salem Alhur & Shaher Alshamari & Judit Oláh & Hanadi Aldreabi, 2022. "Unsupervised Machine Learning to Identify Positive and Negative Themes in Jordanian mHealth Apps," International Journal of E-Services and Mobile Applications (IJESMA), IGI Global, vol. 14(1), pages 1-21, January.
    • Handle: RePEc:igg:jesma0:v:14:y:2022:i:1:p:1-21
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    2. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
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