{"id":25749,"date":"2026-09-03T12:22:44","date_gmt":"2026-09-03T12:22:44","guid":{"rendered":"https:\/\/scientificassociation.org\/?post_type=journal-paper&#038;p=25749"},"modified":"2026-09-03T12:22:44","modified_gmt":"2026-09-03T12:22:44","slug":"an-optimized-classification-of-users-feedback-to-support-mobile-app-evolution-using-convolutional-neural-network","status":"publish","type":"journal-paper","link":"https:\/\/scientificassociation.org\/ar\/journal-paper\/an-optimized-classification-of-users-feedback-to-support-mobile-app-evolution-using-convolutional-neural-network\/","title":{"rendered":"An optimized classification of user&#8217;s feedback to support mobile app evolution using convolutional neural network"},"content":{"rendered":"<div class=\"padding_abstract justify ltr\">Google Play and Apple App Store allow their users to rate apps using a rating scale from one to five, along with a user&#8217;s feedback, i.e., app review. These reviews are a vital source of information, although often present in an unstructured format, making it a challenge to extract useful information from them. However, analyzing these reviews properly can have a transformative impact on both understanding user satisfaction and supporting mobile app evolution for further improvement. Therefore, in this study, we provide an optimized classification of app reviews into four categories, namely bug report, shortcoming &amp; improvement request, feature request, and content request relevant to support mobile app evolution using a Convolutional Neural Network. For this purpose, we used the benchmark dataset developed by Bhatia et al. Further, our optimized classification results show that we achieved a precision of 78.0%, a recall of 84.0%, and thus an F1 score of 81.0%. Moreover, we compared our results with the state-of-the-art, i.e., benchmark results, and it was shown that our Convolutional Neural Network model results outperformed the benchmark results by a significant margin.<\/div>\n","protected":false},"featured_media":25700,"template":"","meta":{"_acf_changed":false},"journal-name":[220],"paper-tag":[277,233,276],"class_list":["post-25749","journal-paper","type-journal-paper","status-publish","has-post-thumbnail","hentry","journal-name-jaiep","paper-tag-april-2026","paper-tag-issue-1","paper-tag-volume-3"],"acf":[],"_links":{"self":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-paper\/25749","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-paper"}],"about":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/types\/journal-paper"}],"wp:attachment":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/media?parent=25749"}],"wp:term":[{"taxonomy":"journal-name","embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-name?post=25749"},{"taxonomy":"paper-tag","embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/paper-tag?post=25749"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}