AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation

Nowadays mobile apps( A.K.A mobile phone applications) have become an integral part of our life. App developers publish their apps to app stores, such as Google Play and Apple Store. These app stores allow users to provide their feedback on apps by posting text reviews and star ratings. Previous studies have shown that these feedbacks contain useful information such as bugs, user experience issues and new feature requests, that help developers to carry out software maintenance and evolution tasks. However, for most popular apps, manually going through each review and extracting useful information is a challenging and laborious task due to the sheer amount of user feedback they are getting daily. In this study, we are in an endeavour to find a solution to the above problem using natural language processing and machine learning techniques to develop a novel approach that would effectively identify, group, summarize and prioritise useful information out of user feedback.