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http://dx.doi.org/10.9716/KITS.2018.17.2.111

Evaluation and Functionality Stems Extraction for App Categorization on Apple iTunes Store by Using Mixed Methods : Data Mining for Categorization Improvement  

Zhang, Chao (College of Business, Hankuk University of Foreign Studies)
Wan, Lili (College of Business, Hankuk University of Foreign Studies)
Publication Information
Journal of Information Technology Services / v.17, no.2, 2018 , pp. 111-128 More about this Journal
Abstract
About 3.9 million apps and 24 primary categories can be approved on Apple iTunes Store. Making accurate categorization can potentially receive many benefits for developers, app stores, and users, such as improving discoverability and receiving long-term revenue. However, current categorization problems may cause usage inefficiency and confusion, especially for cross-attribution, etc. This study focused on evaluating the reliability of app categorization on Apple iTunes Store by using several rounds of inter-rater reliability statistics, locating categorization problems based on Machine Learning, and making more accurate suggestions about representative functionality stems for each primary category. A mixed methods research was performed and total 4905 popular apps were observed. The original categorization was proved to be substantial reliable but need further improvement. The representative functionality stems for each category were identified. This paper may provide some fusion research experience and methodological suggestions in categorization research field and improve app store's categorization in discoverability.
Keywords
Mobile Application; App Categorization; Inter-Rater Reliability; Functionality Stem; Tf-Idf; Machine Learning; Lemmatization; Natural Language Processing;
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