摘要
【目的】利用情感分析技术对引用内容中包含的引用情感进行深层次地发掘和量化,为学术文献内在价值的发现提供更加科学的理论依据和数据支撑。【方法】以知网中检索到的期刊论文为例,通过对施引文献中引用内容的细粒度情感分析和量化,对被引文献的内在学术价值进行深度挖掘,并提出基于引用情感量化的学术评价指标。【结果】实验表明,基于引用情感的学术评价方法比传统的基于被引频次的方法,离散系数高0.12,斯皮尔曼相关系数达到0.981。【局限】由于国内没有完整的全引文数据库,造成数据获取困难,实验样本量较小。【结论】基于细粒度引用情感量化的学术评价方法具有较高的区分度,能更加有效地衡量文献的内在学术价值。
[Objective]This paper uses sentiment analysis technology to deeply excavate and quantify the cited sentiment contained in the cited content,to provide a more scientific theoretical basis and data support for the discovery of the intrinsic value of academic literature.[Methods]Taking the journal papers retrieved in CNKI as an example,through the fine-grained sentiment analysis and sentiment quantification of the citation content in the citing literature,the intrinsic academic value of the cited literature was deeply explored and a new academic evaluation method was proposed.[Results]Experiments showed that the dispersion coefficient based on citation sentiment method was 0.12 higher than the traditional method based on cited frequency,and the Spearman correlation coefficient reached 0.981.[Limitations]Because there is no full text citation database in China,it is difficult to obtain experimental data.The sample size in the experiment is small.[Conclusions]The academic evaluation method based on fine-grained citation sentiment quantification has a higher degree of discrimination and can more effectively measure the intrinsic academic value of the literature.
作者
姜霖
张麒麟
Jiang Lin;Zhang Qilin(School of Economics and Management,Nantong University,Nantong 226019,China;Jiangsu Key Laboratory of Data Engineering and Knowledge Service,Nanjing University,Nanjing 210023,China;Southwest University Library,Chongqing 400715,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2020年第6期129-138,共10页
Data Analysis and Knowledge Discovery
基金
国家社会科学基金项目“大数据环境下学术成果真实价值与影响的实时预测及长期评价研究”(项目编号:19BTQ062)的研究成果之一。
关键词
引用内容
细粒度情感分析
情感量化
学术评价
Citation Content
Fine-Grained Sentiment Analysis
Sentimental Quantification
Academic Evaluation
