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Profiling users via their reviews: an extended systematic mapping study

机译:通过他们的评论分析用户:一个扩展的系统映射研究

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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.
机译:随着大数据和社交网络的广泛发展,用户个人资料字段受到了很多关注。用户分析对于了解各种用户的特征至关重要,有助于更好地了解他们在特定情景中的要求。直接反映人们的思想和意图的用户生成的内容是分析用户的宝贵来源,其中自然用户评论是获取用户要求的宝贵来源,并从学术界和工业中汲取越来越多的关注。然而,作为一种新兴的研究方向,基于审查的用户分析(RBUP)尚未系统地审查,从进一步调查中妨碍研究人员。在这项工作中,我们对审查的用户分析进行了系统的映射研究,重点是调查RBUP的通用分析过程和识别潜在的研究方向。具体而言,在标准化和系统的程序下仔细选择了2478篇论文中的51种纸张。通过对此类论文进行深入分析,我们已经确定了应该遵循的通用过程,以便执行基于审核的用户分析。此外,我们对过程的每个步骤进行多维分析,以便审查当前的研究进展并确定挑战和潜在的研究方向。结果表明,虽然传统方法已经不断改进,但它们不足以释放大规模用户评论的全部潜力,尤其是使用异构数据进行多维用户分析。

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