Profiling users via their reviews: an extended systematic mapping study

被引:0
|
作者
Dong, Xin [1 ]
Li, Tong [1 ]
Song, Rui [1 ]
Ding, Zhiming [1 ]
机构
[1] Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
来源
SOFTWARE AND SYSTEMS MODELING | 2021年 / 20卷 / 01期
基金
国家重点研发计划;
关键词
User-generated reviews; User profiling; Systematic mapping study; Software requirements; CLASSIFICATION; MODEL;
D O I
10.1007/s10270-020-00790-w
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
With the extensive development of big data and social networks, the user profile field has received much attention. User profiling is essential for understanding the characteristics of various users, contributing to better understanding of their requirements in specific scenarios. User-generated contents which directly reflect people's thoughts and intention are a valuable source for profiling users, among which user reviews by nature are invaluable sources for acquiring user requirements and have drawn increasing attention from both academia and industry. However, review-based user profiling (RBUP), as an emerging research direction, has not been systematically reviewed, hindering researchers from further investigation. In this work, we carry out a systematic mapping study on review-based user profiling, with an emphasis on investigating the generic analysis process of RBUP and identifying potential research directions. Specifically, 51 out of 2478 papers were carefully selected for investigation under a standardized and systematic procedure. By carrying out in-depth analysis over such papers, we have identified a generic process that should be followed to perform review-based user profiling. In addition, we perform multi-dimensional analysis on each step of the process in order to review current research progress and identify challenges and potential research directions. The results show that although traditional methods have been continuously improved, they are not sufficient to unleash the full potential of large-scale user reviews, especially the use of heterogeneous data for multi-dimensional user profiling.
引用
收藏
页码:49 / 69
页数:21
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