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

Wan ZHOU; Yong WANG; Cuiyun GAO; Fei YANG

doi:10.1631/FITEE.2100465

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

Special Feature on Intelligent Analysis for Software Data | Updated：2022-05-23

- Emerging topic identification from app reviews via adaptive online biterm topic modeling
- 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
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 5, Pages: 678-691(2022)
- Affiliations：
  
  1.School of Information and Computer, Anhui Polytechnic University, Wuhu241000, China
  2.State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing210000, China
  3.School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen518000, China
  4.Zhejiang Lab, Hangzhou310000, China
- Author bio：
  
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2100465
  CLC： TP311.5
- Received：30 September 2021，
  
  Accepted：02 December 2021，
  
  Published Online：11 April 2022，
  
  Published：0 May 2022
- Accepted：
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Wan ZHOU, Yong WANG, Cuiyun GAO, et al. Emerging topic identification from app reviews via adaptive online biterm topic modeling[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(5): 678-691.
DOI：

Wan ZHOU, Yong WANG, Cuiyun GAO, et al. Emerging topic identification from app reviews via adaptive online biterm topic modeling[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(5): 678-691. DOI： 10.1631/FITEE.2100465.

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。

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).

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references

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