Automatic Classification of Accessibility User Reviews in Android Apps

2022 7th International Conference on Data Science and Machine Learning Applications (CDMA) Pub Date : 2022-03-01 DOI:10.1109/CDMA54072.2022.00027

Wajdi Aljedaani, Mohamed Wiem Mkaouer, S. Ludi, Yasir Javed

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引用次数: 8

Abstract

In recent years, mobile applications have gained popularity for providing information, digital services, and content to users including users with disabilities. However, recent studies have shown that even popular mobile apps are facing issues related to accessibility, which hinders their usability experience for people with disabilities. For discovering these issues in the new app releases, developers consider user reviews published on the official app stores. However, it is a challenging and time-consuming task to identify the type of accessibility-related reviews manually. Therefore, in this study, we have used super-vised learning techniques, namely, Extra Tree Classifier (ETC), Random Forest, Support Vector Classification, Decision Tree, K-Nearest Neighbors (KNN), and Logistic Regression for automated classification of 2,663 Android app reviews based on four types of accessibility guidelines, i.e., Principles, Audio/Images, Design and Focus. Results have shown that the ETC classifier produces the best results in the automated classification of accessibility app reviews with 93% accuracy.

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Android应用中可访问性用户评论的自动分类

近年来，移动应用程序为包括残疾用户在内的用户提供信息、数字服务和内容而变得越来越受欢迎。然而，最近的研究表明，即使是流行的移动应用也面临着与可访问性相关的问题，这阻碍了它们对残疾人的可用性体验。为了在新应用发布中发现这些问题，开发者会考虑发布在官方应用商店上的用户评论。然而，手动确定与可访问性相关的审查类型是一项具有挑战性且耗时的任务。因此，在本研究中，我们使用了监督学习技术，即额外树分类器(ETC)、随机森林、支持向量分类、决策树、k近邻(KNN)和逻辑回归，基于四种可访问性准则，即原则、音频/图像、设计和焦点，对2663条Android应用评论进行了自动分类。结果表明，ETC分类器在可访问性应用程序评论的自动分类中产生了最好的结果，准确率为93%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 7th International Conference on Data Science and Machine Learning Applications (CDMA)

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