@inproceedings{5f288049cb944e34a2eb38d9b6fc9cea,
title = "Neural Sentiment Analysis of User Reviews to Predict User Ratings",
abstract = "The significance of user satisfaction is increasing in the competitive open source software (OSS) market. Application stores let users send their feedbacks for applications, which are in the form of user reviews or ratings. Developers are informed about bugs or any additional requirements with the help of this feedback and use it to increase the quality of the software. Moreover, potential users rely on this information as a success indicator to decide downloading the applications. Since it is usually costly to read all the reviews and evaluate their content, the ratings are taken as the base for the assessment. This makes the consistency of the contents with the ratings of the reviews important for healthy evaluation of the applications. In this study, we use recurrent neural networks to analyze the reviews automatically, and thereby predict the user ratings based on the reviews. We apply transfer learning from a huge volume, gold dataset of Amazon Customer Reviews. We evaluate the performance of our model on three mobile OSS applications in the Google Play Store and compare the predicted ratings and the original ratings of the users. Eventually, the predicted ratings have an accuracy of 87.61\% compared to the original ratings of the users, which seems promising to obtain the ratings from the reviews especially if the former is absent or its consistency with the reviews is weak.",
keywords = "Deep Learning, OSS, Open Source, Sentiment Analysis, Software Quality, Transfer Learning, User Satisfaction",
author = "Bahar Gezici and Necva Bolucu and Ayca Tarhan and Burcu Can",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 4th International Conference on Computer Science and Engineering, UBMK 2019 ; Conference date: 11-09-2019 Through 15-09-2019",
year = "2019",
month = sep,
doi = "10.1109/UBMK.2019.8907234",
language = "English",
series = "UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "629--634",
booktitle = "UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering",
address = "United States",
}