Abstract
Detecting user’s sentiment and preference (e.g., complain or new feature wanted) timely and precisely is crucial for developers to improve their apps correspondingly to win the competitive mobile-app market. In this paper, we propose a novel and automated framework OSPAci, which aims to identify user’s sentiment and preference effectively based on online user reviews. OSPAci uses sentiment analysis and natural language processing techniques to obtain sentence-level sentiment scores and fine-grained user preference from mobile app reviews. Then, it analysis the evolution of user’s sentiment trend and preference. Finally, the user sentiment trend and preference correlation is analyzed along the time dimension, thus this model can be used to monitor user’s sentiment tendency and preference almost in time. We evaluate the feasibility and performance of OSPAci by using real Google play’s user reviews. The experimental results show that OSPAci can effectively and efficiently identify the user’s sentiment tendency and detect user preference timely and precisely.
Supervised by Shizhan Chen, College of Intelligence and Computing, Tianjin University, China, shizhan@tju.edu.cn, Shiping Chen, CSIRO Data61, Australia, Shiping.Chen@csiro.au, Xiao Xue, College of Intelligence and Computing, Tianjin University, China, Zhiyong Feng, College of Intelligence and Computing, Tianjin University, China, zyfeng@tju.edu.cn
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Acknowledgements
This work is supported by the National Key R&D Program of China grant No. 2017YFB1401201, the National Natural Science Foundation of China grant No. 61572350, the National Natural Science Key Foundation of China grant No. 61832014 and the Shenzhen Science and Technology Foundation (JCYJ20170816093943197).
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Xiao, J. (2020). OSPAci: Online Sentiment-Preference Analysis of User Reviews for Continues App Improvement. In: Yangui, S., et al. Service-Oriented Computing – ICSOC 2019 Workshops. ICSOC 2019. Lecture Notes in Computer Science(), vol 12019. Springer, Cham. https://doi.org/10.1007/978-3-030-45989-5_23
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DOI: https://doi.org/10.1007/978-3-030-45989-5_23
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