基于改进的TF*IDF方法分析学科研究热点——以情报学为例  

Analyze of Subject Research Hot Spots Based on An Improved Algorithm of TF*IDF——Taking Information Science for Example

作　　者：刘小慧[1] 李长玲[1] 冯志刚[1] 

机构地区：[1]山东理工大学科技信息研究所,山东淄博255049

出　　处：《情报科学》2017年第7期82-87,共6页Information Science

基　　金：国家社科基金项目(16BTQ078)

摘　　要：【目的/意义】提出一种TF*IDF改进算法,用于全文分词后的语词权重计算,提取高权重语词,分析学科研究热点。【方法/过程】以万方数据库中2015年《情报学报》的载文为例,对每篇文章全文分词,用改进的TF*IDF方法计算语词权重。【结果/结论】发现该改进算法准确可行,且运用该方法分析得到,用户研究、大数据、情报学、社交网络、技术领域、文献作者、突发事件、零被引等,是2015年情报学的研究热点。[Purpose/significance] This paper comes up with an improved algorithm of TF＊IDF to calculate the weight of words from papers after the word segmentation, extract high weight words and determine the research hot spots. [ Method/ process]Taking the articles published in Journal of the China Society for Scientific and Technical Information which from Wan Fang data platform in 2015 as an example, segment the word of every article, then calculate the weight of words by the improved algorithm of TF＊IDF. [Result/conclusion]This improved algorithm is accurate and feasible. And through analyz- ing, it is found that the research hot spots of information science in 2015 are user researches,big data,informatics,social net- work,technical field,the author,emergency and non-citation and so on.

关 键 词：研究热点 TF＊IDF 全文分词 情报学 

分 类 号：G250.2[文化科学—图书馆学]