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CLC number: TP311.5

On-line Access: 2022-05-19

Received: 2021-09-30

Revision Accepted: 2022-05-19

Crosschecked: 2021-12-02

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Citations:  Bibtex RefMan EndNote GB/T7714






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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.5 P.678-691


Emerging topic identification from app reviews via adaptive online biterm topic modeling

Author(s):  Wan ZHOU, Yong WANG, Cuiyun GAO, Fei YANG

Affiliation(s):  School of Information and Computer, Anhui Polytechnic University, Wuhu 241000, China; more

Corresponding email(s):   yongwang@ahpu.edu.cn

Key Words:  App reviews, Emerging topic identification, Topic model, Natural language processing

Wan ZHOU, Yong WANG, Cuiyun GAO, Fei YANG. Emerging topic identification from app reviews via adaptive online biterm topic modeling[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(5): 678-691.

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A1 - Wan ZHOU
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A1 - Fei YANG
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DOI - 10.1631/FITEE.2100465

Emerging topics in app reviews highlight the topics (e.g., software bugs) with which users are concerned during certain periods. Identifying emerging topics accurately, and in a timely manner, could help developers more effectively update apps. Methods for identifying emerging topics in app reviews based on topic models or clustering methods have been proposed in the literature. However, the accuracy of emerging topic identification is reduced because reviews are short in length and offer limited information. To solve this problem, an improved emerging topic identification (IETI) approach is proposed in this work. Specifically, we adopt natural language processing techniques to reduce noisy data, and identify emerging topics in app reviews using the adaptive online biterm topic model. Then we interpret the implicature of emerging topics through relevant phrases and sentences. We adopt the official app changelogs as ground truth, and evaluate IETI in six common apps. The experimental results indicate that IETI is more accurate than the baseline in identifying emerging topics, with improvements in the F1 score of 0.126 for phrase labels and 0.061 for sentence labels. Finally, we release the codes of IETI on Github (https://github.com/wanizhou/IETI).




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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