中国管理科学领域热点主题识别与趋势预测

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Abstract It is important for scholars and research administrators to grasp the direction of research and allocate research funds effectively,which can identify and anticipate research hotspots and their trends in management science.In this study,the literature published in important management science journals designated by National Natural Science Foundation of China (NSFC)in recent 20 years was used as the analysis data source,and the “Sleeping Beauty”literature identification method in citation analysis was used for keyword semantic clustering to identify the research topic.Based on the characteristics of theme dormancy and hotness,the theme class is divided into four categories:sleeping beauty feature hotspots,mature hotspots,potential hotspots and others.The beauty coefficient was used to identify the emergence points of sleeping beauty themes,combined hotspot emergence points to predict the research hotness of the theme categories,and the cumulative growth curve was used to verify its reasonableness.The results show that the four themes of innovation capability,relationship network,behavioral intention and resources and environmental protection belong to the sleeping beauty hot topics.Entrepreneurial talent,international trade,industrial automobiles and behavioral psychology are potential hot topics,which are late to emerge,have grown faster in recent years and are likely to become emerging hot topics.

Key words： Keywords clustering Topic segmentation Research hotspot Trend prediction

Received: 19 October 2020

PACS:

G353.1

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	Yu Hui
	Liang Zhentao
	Zhang Yufan

Cite this article:

Yu Hui,Liang Zhentao,Zhang Yufan. Identifying Hot Topics and Trend Prediction of Management Science in China[J]. , 2021, 2(8): 145-156.

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http://www.zgkjlt.org.cn/EN/ OR http://www.zgkjlt.org.cn/EN/Y2021/V2/I8/145

[1]WALTMAN L,ECK N J.A new methodology for constructing a publication-level classification system of science[J].Journal of the American society for information science and technology,2012,63 (12):2378-2392.
[2]张颖怡,章成志,陈果.基于关键词的学术文本聚类集成研究[J].情报学报,2019,38 (8):860-871.
[3]苏新宁.图书馆、情报与文献学学术影响力研究报告 (2000—2004)——基于CSSCI的分析[J].情报学报,2006,25 (2):131-153.
[4]SMALL H.Co-citation in the scientific literature:a new measure of the relationship between two documents[J].Journal of the American society for information sciences,1973,24 (4):265-269.
[5]MARTYN J.Bibliographic coupling[J].Journal of documentation,1964,20 (4):236.
[6]BRUSH S G.The use of citation data in writing the history of science[J].ISIS,1965,56 (186):487.
[7]耿海英.共引分析方法及其应用研究[D].北京:中国科学院研究生院 (文献情报中心),2007.
[8]刘则渊,陈悦,侯海燕,等.科学知识图谱方法与应用[M].北京:人民出版社,2008.
[9]张长宏,张明亮.基于内容和引用的科学领域主题的发现[J].哈尔滨师范大学自然科学学报,2017,33 (2):100-103.
[10]黄文彬,王冰璐,步一,等.关键词共引分析的科学计量方法研究[J].情报资料工作,2018 (2):37-42.
[11]唐果媛,张薇.国内外共词分析法研究的发展与分析[J].图书情报工作,2014,58 (22):138-145.
[12]李海林,邬先利.基于时间序列聚类的主题发现与演化分析研究[J].情报学报,2019,38 (10):1041-1050.
[13]高继平,丁堃,潘云涛,等.多词共现分析方法的实现及其在研究热点识别中的应用[J].图书情报工作,2014,58 (24):80-85.
[14]DONOHUE J C.Understanding scientific literature[J].Information storage & retrieval,1973,10 (11):420-421.
[15]孙清兰.高频、低频词的界分及词频估计方法[J].情报科学,1992 (2):28-32.
[16]吴健,李子运,王洪梅.基于关键词共现聚类的深阅读研究热点分析[J].图书馆建设,2016 (12):53-59.
[17]CHOI S,PARK H W.An exploratory approach to a twitter-based community centered on a political goal in South Korea:who organized it,what they shared,and how they acted[J].New media & society,2013,16 (1):129-148.
[18]庄建昌,武娇,顾兴全,等.基于热词语义聚类的领域特征挖掘方法[J].中国计量大学学报,2019,30 (2):210-218.
[19]章成志,梁勇.基于主题聚类的学科研究热点及其趋势监测方法[J].情报学报,2010,29 (2):342-349.
[20]杜建,武夷山.基于被引速率指标识别睡美人文献及其 “王子”——以2014年诺贝尔化学奖得主Stefan Hell的睡美人文献为例[J].情报学报,2015,34 (5):508-521.
[21]张靖雯,孙建军,闵超.引文起飞的定义与量化方法研究[J].情报学报,2019,38 (8):786-797.
[22]HU K,QI K,YANG S,et al.Identifying the “ghost city”of domain topics in a keyword semantic space combining citations[J].Scientometrics,2018,114 (3):1141-1157.
[23]HU K,LUO Q,QI K,et al.Understanding the topic evolution of scientific literatures like an evolving city:using google word2vec model and spatial autocorrelation analysis[J].Information processing & management,2019,56 (4):1185-1203.
[24]MAYUR A,MABE M A.Impact factors:use and abuse[J].Medicina,2003,63 (4):347-354.
[25]MIN C,DING Y,LI J,et al.Innovation or imitation:the diffusion of citations[J].Journal of the association for information science and technology,2018,69 (10).
[26]RAAN A F J V.Sleeping beauties in science[J].Scientometrics,2004,59 (3):467-472.
[27]KE Q,FERRARA E,RADICCHI F,et al.Defining and dentifying sleeping beauties in science[J].Proceedings of the national academy of sciences,2015,112 (24):7426-7431.
[28]杜建,武夷山.文献引文轨迹:分类及测度[J].情报理论与实践,2015,38 (7):52-58.
[29]屈文建,胡志伟,周小渝.面向图情学科热点高被引论文引文曲线特征分析[J].情报杂志,2017,36 (8):138-143.
[30]JIANG L,SHI D,ZHAO S X,et al.A study of the “heartbeat spectra”for “sleeping beauties”[J].Journal of informetrics,2014,8 (3):493-502.
[31]徐以鸿,朱涛.机构知识库内容快速建设方法[J].现代情报,2011,31 (4):148-151.
[32]刘振华.高校图书馆数字资源利用存在的问题及对策——基于哈尔滨市高校图书馆数字资源利用情况调查的实证研究[J].高校图书馆工作,2009,29 (5):68-69.
[33]SONG Y,SHI S,LI J,et al.Directional skip-gram:explicitly distinguishing left and right context for word embeddings[M].2018:175-180.
[34]吴广建,章剑林,袁丁.基于K-means的手肘法自动获取K值方法研究[J].软件,2019,40 (5):167-170.
[35]QING K,EMILIO F,FILIPPO R,et al.Defining and identifying sleeping beauties in science[J].Proceedings of the national academy of sciences of the United States of America,2015,112 (24):7426-7431.
[36]COSTAS R,LEEUWEN T N V,RAAN A F J V.Is scientific literature subject to a sell-by-date? A general methodology to analyze the durability of scientific documents[J].Journal of the American society for information science & technology,2010,61 (2):329-339.
[37]郭斐,鄢小燕.睡美人文献识别方法分析与改进构想[J].图书情报工作,2016,60 (8):93-98.
[38]VAUGHAN L,YOU J.Word co-occurrences on webpages as a measure of the relatedness of organizations:a new webometrics concept[J].Journal of informetrics,2010,4 (4):483-491.