Paper Info
Title
Finding the Needle in a Haystack: On the Automatic Identification of Accessibility User Reviews
Abstract
ABSTRACT In recent years, mobile accessibility has become an important trend with the goal of allowing all users the possibility of using any app without many limitations. User reviews include insights that are useful for app evolution. However, with the increase in the amount of received reviews, manually analyzing them is tedious and time-consuming, especially when searching for accessibility reviews. The goal of this paper is to support the automated identification of accessibility in user reviews, to help technology professionals in prioritizing their handling, and thus, creating more inclusive apps. Particularly, we design a model that takes as input accessibility user reviews, learns their keyword-based features, in order to make a binary decision, for a given review, on whether it is about accessibility or not. The model is evaluated using a total of 5,326 mobile app reviews. The findings show that (1) our model can accurately identify accessibility reviews, outperforming two baselines, namely keyword-based detector and a random classifier; (2) our model achieves an accuracy of 85% with relatively small training dataset; however, the accuracy improves as we increase the size of the training dataset.
Year
DOI
Venue
2021
10.1145/3411764.3445281
Conference on Human Factors in Computing Systems
Keywords
DocType
Citations 
Mobile application, user review, accessibility, machine learning
Conference
1
PageRank 
References 
Authors
0.35
45
5
Name
Order
Citations
PageRank
Eman Abdullah AlOmar110.35
Wajdi Aljedaani2122.21
Murtaza Tamjeed310.35
Mohamed Wiem Mkaouer422828.58
Yasmine N. El-Glaly5275.75