Abstract
In the process of software development, feedback from users is important for developers. Most of this information is in the form of a text. In order to utilize this information, Natural Language Processing approach is needed. In this study, we build and evaluate a machine learning model for classification of application reviews. We used Naive Bayes, Logistic Regression, and BERT. As a result, the BERT classifier showed the highest performance with Precision 0.7237, Recall 0.7286 and F1-Score 0.7173.
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Notes
- 1.
App review analysis, https://mast.informatik.uni-hamburg.de/app-review-analysis/.
- 2.
- 3.
NLTK, https://www.nltk.org/.
- 4.
FastText—English word vectors, https://fasttext.cc/docs/en/english-vectors.html.
References
S. Krusche, B. Bruegge, User feedback in mobile development. in Proceedings of the 2nd International Workshop on Mobile Development Lifecycle MobileDeLi’14 (2014), pp. 25–26
W. Maalej, H. Nabil, Bug report, feature request, or simply praise? on automatically classifying app reviews. in Proceedings of the IEEE 23rd International Requirements Engineering Conference (2015), pp. 116–125
M. Lu, P. Liang, Automatic classification of non-functional requirements from augmented app user reviews. in Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering (2017), pp. 344–353
Y. Zhang, R. Jin, Z.-H. Zhou, Understanding bag-of-words model: a statistical framework. Int. J. Mach. Learn. Cybern. 1, 43–52 (2010)
A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, in Bag of Tricks for Efficient Text Classification. arXiv arXiv:1607.01759 (2016)
P. Bojanowski, E. Grave, A. Joulin, T. Mikolov, Enriching word vectors with subword information. arXiv, arXiv:1607.04606 (2016)
J. Devlin, M.W. Chang, K. Lee, K. Toutanova, BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin, Attention is all you need. arXiv arXiv:1706.03762 (2017)
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Yamada, Y., Hazeyama, A. (2022). Building of an Application Reviews Classifier by BERT and Its Evaluation. In: Virvou, M., Tsihrintzis, G.A., Bourbakis, N.G., Jain, L.C. (eds) Handbook on Artificial Intelligence-Empowered Applied Software Engineering. Artificial Intelligence-Enhanced Software and Systems Engineering, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-031-08202-3_5
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DOI: https://doi.org/10.1007/978-3-031-08202-3_5
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