PREDICTING APPLICATION RATINGS BASED ON USER REVIEWS
DOI:
https://doi.org/10.24867/22BE04MijatovicKeywords:
machine learning, recurrent neural networks, ensemble learningAbstract
This paper presents a specification, implementation and evaluation of a system that predicts the rating of application based on textual comments. Two approaches were compared - recurrent neural networks and ensemble models.
References
[1] D. Monett and H. Stolte, “Predicting Star Ratings based on Annotated Reviews of Mobile Apps,” Oct. 2016, pp. 421–428. doi: 10.15439/2016F141.
[2] M. Umer, I. Ashraf, A. Mehmood, S. Ullah, and G. S. Choi, “Predicting numeric ratings for Google apps using text features and ensemble learning,” ETRI Journal, vol. 43, no. 1, pp. 95–108, Feb. 2021, doi: 10.4218/etrij.2019-0443.
[3] B. Gezici, N. Bolucu, A. Tarhan, and B. Can, “Neural Sentiment Analysis of User Reviews to Predict User Ratings,” in 2019 4th International Conference on Computer Science and Engineering (UBMK), Samsun, Turkey, Sep. 2019, pp. 629–634. doi: 10.1109/UBMK.2019.8907234.
[4] https://www.kaggle.com/datasets/prakharrathi25/google-play-store-reviews
[2] M. Umer, I. Ashraf, A. Mehmood, S. Ullah, and G. S. Choi, “Predicting numeric ratings for Google apps using text features and ensemble learning,” ETRI Journal, vol. 43, no. 1, pp. 95–108, Feb. 2021, doi: 10.4218/etrij.2019-0443.
[3] B. Gezici, N. Bolucu, A. Tarhan, and B. Can, “Neural Sentiment Analysis of User Reviews to Predict User Ratings,” in 2019 4th International Conference on Computer Science and Engineering (UBMK), Samsun, Turkey, Sep. 2019, pp. 629–634. doi: 10.1109/UBMK.2019.8907234.
[4] https://www.kaggle.com/datasets/prakharrathi25/google-play-store-reviews
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Published
2023-03-04
Issue
Section
Electrotechnical and Computer Engineering
