摘要
目前电商网站、社交媒体网站上包含大量的图书评论数据。挖掘在线图书评论,对于图书的全面评价、用户购买决策等都具有重要的作用。然而,目前基于图书评论的图书评价研究主要依据单一的评论平台,尚未利用多个平台的评论数据,在一定程度上造成图书评价结果的片面性。为此,本文量化不同平台图书评论差异的基础上,提出一种整合不同平台的图书综合影响力评价方法,即通过对社交平台、电子商务网站等数据源图书评论的采集与整合,进行图书综合影响力的评价。本文以4个学科的348本图书为例,从图书属性层面整合多源数据,分析不同整合策略所得图书评价结果的差异及原因。与基于单一平台的图书评价结果对比分析表明,整合不同平台数据的评价方法能够更加全面地评价图书影响力,降低传统方法的局限性。
Currently,online book reviews are abundant and widely available on e-commerce and social network websites.Mining these reviews is important for comprehensively evaluating books and persuading customers to make purchasing decisions.However,most prior research based on book reviews mainly focused on a single review platform instead of using review data from multiple platforms,resulting,to some extent,in a one-sided evaluation.Hence,based on the quantitative analysis of book reviews across different platforms,we put forth one method to measure the comprehensive impacts of books utilizing multi-source data,which provides fresh ideas for book evaluation by collecting and integrating book reviews from social networking platforms and e-commerce websites.In this study,by taking 348 books in four subjects as an example and integrating multi-source reviews of them from an aspect perspective,we analyzed the differences between book evaluation results obtained through different integration strategies.In the end,a comparison between the results obtained by our method and traditional methods based on a single platform shows that combing multiple reviews can evaluate a book’s impact more comprehensively and avoid the limitations of traditional methods.
作者
章成志
童甜甜
周清清
Zhang Chengzhi;Tong Tiantian;Zhou Qingqing(Department of Information Management,Nanjing University of Science&Technology,Nanjing 210094;Institute of Scientific and Technical Information of China(ISTIC),Beijing 100038)
出处
《情报学报》
CSSCI
CSCD
北大核心
2018年第9期861-873,共13页
Journal of the China Society for Scientific and Technical Information
基金
中国科学技术信息研究所情报工程实验室开放基金项目"基于多源异构数据融合的学术专著影响力评估研究"
关键词
图书评价
多源数据
在线图书评论
属性级情感分析
book evaluation
multi-source heterogeneous data
online book reviews
aspect-level sentiment analysis