Full Text:   <779>

Summary:  <36>

CLC number: TP311.5

On-line Access: 2022-05-19

Received: 2021-09-30

Revision Accepted: 2022-05-19

Crosschecked: 2021-12-02

Cited: 0

Clicked: 1159

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Wan ZHOU

https://orcid.org/0000-0002-5024-958X

Yong WANG

https://orcid.org/0000-0002-2719-1017

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

http://doi.org/10.1631/FITEE.2100465


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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Abstract: 
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).

基于自适应在线双词主题模型的应用程序评论新兴主题识别

周芄1,王勇1,2,高翠芸3,杨非4
1安徽工程大学计算机与信息学院,中国芜湖市,241000
2南京大学计算机软件新技术国家重点实验室,中国南京市,210000
3哈尔滨工业大学(深圳)计算机科学与技术学院,中国深圳市,518000
4之江实验室,中国杭州市,310000
摘要:应用程序评论中的新兴主题突出了用户在一定时期内关注的主题(如软件漏洞)。准确、及时地识别新兴主题能帮助开发者更有效地更新应用程序。已有文献基于主题模型或聚类方法识别应用程序评论中的新兴主题。然而,由于评论文本长度较短,提供的信息有限,新兴主题识别准确率较低。为解决该问题,提出一种改进的新兴主题识别方法(IETI)。首先采用自然语言处理技术减少评论文本中的噪音数据,然后使用自适应在线双词主题模型识别评论中的新兴主题。最后利用新兴主题中相关的短语和句子解释新兴主题的含义。采用官方更新日志作为新兴主题的评估标准,选择6个常见的应用程序对IETI进行评估。实验结果表明,IETI在识别新兴主题方面优于传统方法,短语标签F1值增量为0.126,句子标签F1值增量为0.061。我们在Github(https://github.com/wanizhou/IETI)上发布了IETI的代码。

关键词:应用程序评论;新兴主题识别;主题模型;自然语言处理

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

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