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An Intelligent Analysis and Service Design Method for User Feedback
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[1]Wang Ye,Zhou Siyuan,Weng Zhiyuan,et al.An Intelligent Analysis and Service Design Method for User Feedback[J].Journal of Zhengzhou University (Engineering Science),2023,44(03):58-63.[doi:10.13705/j.issn.1671-6833.2022.06.004]
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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 03 Page number: 58-63 Column: Public date: 2023-04-30
Title:
An Intelligent Analysis and Service Design Method for User Feedback
Author(s):
Wang Ye1;  Zhou Siyuan1;  Weng Zhiyuan2;  Chen Junwu1
1.School of Computer and Information Engineering, Zhejiang University of Technology, Hangzhou 310018, Zhejiang, 2.Department of Computer Science and Engineering, University of Nebhauska, Lincoln City, Inner Branch

Keywords:
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CLC:
TP311
DOI:
10.13705/j.issn.1671-6833.2022.06.004
Abstract:
In recent years, intelligent services in various fields have been paid more and more attention and achieved rapid development. There are a lot of user comment data in the use feedback of each application software. It is of great significance to mine meaningful user demands from these large numbers of user comment data with varying quality. At present, the existing requirements intelligent classification methods do not improve the reusability and application value from the perspective of service computing. Therefore, it is an important direction to study a method of intelligent mining of user requirements and design it as reusable service to help the iterative update of application software. This paper focuses on the design method of an intelligent service to mine user needs, selects user review data of multiple apps on IOS platform, conducts intelligent mining and classification on them, and analyzes the potential demands. First, analyze the user demand category, define the demand category Then, the user data is crawled, cleaned and annotated to form a software classification data set. Then, the effect of mainstream deep learning (TextCNN, BiLSTM_Attention and BERT) on intelligent classification of user comment data is explored through experiments, and the classification results are prioritized. Finally, the intelligent service is designed and packaged as a Python package that can be invoked. Through experimental comparison, it is found that BERT model performs better in Precision, Recall and F1-Measure.
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Last Update: 2023-05-09
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