Finding the Needle in a Haystack: On the Automatic Identification of Accessibility User Reviews
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 a...
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| creator | AlOmar, Eman Abdullah Aljedaani, Wajdi Tamjeed, Murtaza Mkaouer, Mohamed Wiem Elglaly, Yasmine N |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2210.09947 |
| format | Article |
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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;
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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;
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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.</abstract><doi>10.48550/arxiv.2210.09947</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Software Engineering |
| title | Finding the Needle in a Haystack: On the Automatic Identification of Accessibility User Reviews |
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