TY - GEN
T1 - How do users like this feature? A fine grained sentiment analysis of App reviews
AU - Guzman, Emitza
AU - Maalej, Walid
PY - 2014/9/26
Y1 - 2014/9/26
N2 - App stores allow users to submit feedback for downloaded apps in form of star ratings and text reviews. Recent studies analyzed this feedback and found that it includes information useful for app developers, such as user requirements, ideas for improvements, user sentiments about specific features, and descriptions of experiences with these features. However, for many apps, the amount of reviews is too large to be processed manually and their quality varies largely. The star ratings are given to the whole app and developers do not have a mean to analyze the feedback for the single features. In this paper we propose an automated approach that helps developers filter, aggregate, and analyze user reviews. We use natural language processing techniques to identify fine-grained app features in the reviews. We then extract the user sentiments about the identified features and give them a general score across all reviews. Finally, we use topic modeling techniques to group fine-grained features into more meaningful high-level features. We evaluated our approach with 7 apps from the Apple App Store and Google Play Store and compared its results with a manually, peer-conducted analysis of the reviews. On average, our approach has a precision of 0.59 and a recall of 0.51. The extracted features were coherent and relevant to requirements evolution tasks. Our approach can help app developers to systematically analyze user opinions about single features and filter irrelevant reviews.
AB - App stores allow users to submit feedback for downloaded apps in form of star ratings and text reviews. Recent studies analyzed this feedback and found that it includes information useful for app developers, such as user requirements, ideas for improvements, user sentiments about specific features, and descriptions of experiences with these features. However, for many apps, the amount of reviews is too large to be processed manually and their quality varies largely. The star ratings are given to the whole app and developers do not have a mean to analyze the feedback for the single features. In this paper we propose an automated approach that helps developers filter, aggregate, and analyze user reviews. We use natural language processing techniques to identify fine-grained app features in the reviews. We then extract the user sentiments about the identified features and give them a general score across all reviews. Finally, we use topic modeling techniques to group fine-grained features into more meaningful high-level features. We evaluated our approach with 7 apps from the Apple App Store and Google Play Store and compared its results with a manually, peer-conducted analysis of the reviews. On average, our approach has a precision of 0.59 and a recall of 0.51. The extracted features were coherent and relevant to requirements evolution tasks. Our approach can help app developers to systematically analyze user opinions about single features and filter irrelevant reviews.
UR - https://www.scopus.com/pages/publications/84909943417
UR - https://www.scopus.com/pages/publications/84909943417#tab=citedBy
U2 - 10.1109/RE.2014.6912257
DO - 10.1109/RE.2014.6912257
M3 - Conference contribution
AN - SCOPUS:84909943417
T3 - 2014 IEEE 22nd International Requirements Engineering Conference, RE 2014 - Proceedings
SP - 153
EP - 162
BT - 2014 IEEE 22nd International Requirements Engineering Conference, RE 2014 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE 22nd International Requirements Engineering Conference, RE 2014
Y2 - 25 August 2014 through 29 August 2014
ER -