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文章针对共词方法在内容分析中存在的问题,提出了利用引文耦合关系增强共词分析效果的方法。首先,统计了关键词在共现和引文耦合两个方面的频次关系;其次,使用Z分值对两个频次矩阵进行了标准化,消除量纲数量级差异造成的不可比性;然后,基于标准化矩阵计算关键词间的Pearson相关性,用关键词在共现和引文耦合两个维度上相关系数的均值表示关键词的总关联强度;再次,将关键词关联强度矩阵导入SPSS进行聚类,根据聚类结果对关键词进行主题领域划分;最后,以ESI农业科学领域高被引论文为例比较了引文耦合增强的共词方法与传统共词方法的分析效果,结果表明改进的方法能更好地突出关键词间的相关关系,有效地提高学科情报研究中主题领域划分的准确性。
In this paper, according to the existing problems in content analysis of co-word method, this paper proposes a method to enhance the effect of co-word analysis by using the citation coupling relationship. First of all, we calculated the frequency relationship between the keywords in co-occurrence and citation coupling. Secondly, we normalized the two frequency matrices by using Z scores to eliminate the incommensurability caused by the difference in order of magnitude. Then, The Pearson correlation between keywords is calculated, and the average relevance of the keywords is represented by the mean of the correlation coefficients between the co-occurrence and the citation coupling. Thirdly, the keyword relevance matrix is imported into SPSS for clustering. According to the poly Finally, taking the highly cited papers in ESI agricultural science as an example, this paper compares the analysis results of the co-word method and the traditional co-word method with enhanced citation coupling. The results show that the improved method can better Highlighting the correlation between key words, effectively improve the accuracy of thematic areas in the study of subject information.