TY - GEN
T1 - Finding the needle in a haystack
T2 - 10th International Conference on Materials Processing and Characterisation, ICMPC 2020
AU - AlOmar, Eman Abdullah
AU - Aljedaani, Wajdi
AU - Tamjeed, Murtaza
AU - Mkaouer, Mohamed Wiem
AU - El-Glaly, Yasmine N.
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/5/6
Y1 - 2021/5/6
N2 - 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 timeconsuming, 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.
AB - 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 timeconsuming, 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.
KW - Accessibility
KW - Machine learning
KW - Mobile application
KW - User review
UR - http://www.scopus.com/inward/record.url?scp=85106705949&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85106705949&partnerID=8YFLogxK
U2 - 10.1145/3411764.3445281
DO - 10.1145/3411764.3445281
M3 - Conference contribution
AN - SCOPUS:85106705949
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Y2 - 21 February 2020 through 23 February 2020
ER -