Based on the foundation laid by the h-index we introduce and study the R- and AR-indices. These new indices eliminate some of the disadvantages of the h-index, especially when they are used in combina-tion with the h-...Based on the foundation laid by the h-index we introduce and study the R- and AR-indices. These new indices eliminate some of the disadvantages of the h-index, especially when they are used in combina-tion with the h-index. The R-index measures the h-core’s citation intensity, while AR goes one step further and takes the age of publications into account. This allows for an index that can actually in-crease and decrease over time. We propose the pair (h, AR) as a meaningful indicator for research evaluation. We further prove a relation characterizing the h-index in the power law model.展开更多
为了解国内外情感分析领域的研究状况,揭示该领域的知识结构、研究热点与发展动态,本文采用共被引分析、聚类分析、共词分析、战略坐标分析等方法,借助CiteSpace、UCINET、BICOMB、SPSS等软件,对Web of Science数据库收录的以情感分析...为了解国内外情感分析领域的研究状况,揭示该领域的知识结构、研究热点与发展动态,本文采用共被引分析、聚类分析、共词分析、战略坐标分析等方法,借助CiteSpace、UCINET、BICOMB、SPSS等软件,对Web of Science数据库收录的以情感分析为主题的相关文献进行计量分析与知识图谱绘制。分析结果表明,情感分析的应用、深度学习与神经网络、电子商务下的产品评论、事物情感特征评分、社交网络下用户生成内容、语义定向广告技术以及文本语言属性分析构建了情感分析的知识结构,产品评论与口碑、数据挖掘与人工智能、无监督学习、HadoopMapReduce与支持向量机以及神经网络与深度学习为该领域的研究热点,而顾客评论、推荐系统、极性分类、主题模型、电影评论、推特数据将是未来该领域主要研究方向。展开更多
基金Supported by a Major State Basic Research Special Program China under grant (No. 2004CCC00400)National Natural Science Foundation of China (Grant No. 70376019)
文摘Based on the foundation laid by the h-index we introduce and study the R- and AR-indices. These new indices eliminate some of the disadvantages of the h-index, especially when they are used in combina-tion with the h-index. The R-index measures the h-core’s citation intensity, while AR goes one step further and takes the age of publications into account. This allows for an index that can actually in-crease and decrease over time. We propose the pair (h, AR) as a meaningful indicator for research evaluation. We further prove a relation characterizing the h-index in the power law model.
文摘为了解国内外情感分析领域的研究状况,揭示该领域的知识结构、研究热点与发展动态,本文采用共被引分析、聚类分析、共词分析、战略坐标分析等方法,借助CiteSpace、UCINET、BICOMB、SPSS等软件,对Web of Science数据库收录的以情感分析为主题的相关文献进行计量分析与知识图谱绘制。分析结果表明,情感分析的应用、深度学习与神经网络、电子商务下的产品评论、事物情感特征评分、社交网络下用户生成内容、语义定向广告技术以及文本语言属性分析构建了情感分析的知识结构,产品评论与口碑、数据挖掘与人工智能、无监督学习、HadoopMapReduce与支持向量机以及神经网络与深度学习为该领域的研究热点,而顾客评论、推荐系统、极性分类、主题模型、电影评论、推特数据将是未来该领域主要研究方向。
