可视化视域下我国科普政策文本内容挖掘分析

The Content Mining of Science Popularization Policies in China from aPerspective of Visualization

中文关键词: 可视化 科普政策 文本内容挖掘 共词分析

英文关键词: visualization  popular science policy  content mining  co-word analysis

中文摘要:

      为了挖掘出科普政策文本的核心内容及其存在问题，本研究利用ROSTCM 词频分析软件对科普政 策文本关键词进行了提取，构建了关键词共词矩阵、相关矩阵和相异矩阵，之后运用Ucinet 和SPSS 软件对 关键词进行了聚类分析、多维尺度分析和社会网络分析。从聚类分析得出，科普政策内容关键词可分为八大 类，分别为青少年和农民的培训和指导、科普资源共享、基础设施建设与经费投入等，表明科普政策内容涉 及的主题广泛。在多维尺度分析中，将我国科普政策内容分为四大词团，我国科普政策内容聚焦点清晰可见。 对关键词进行了网络密度分析得出，科普政策内容设计存在缺陷。最后提出加强政策内容的层次性、提高政 策内容的系统性等对策建议。

英文摘要:

      In order to excavate the core content and its problems of science popularization policy， ROSTCM，a kind of word frequency analysis software，is used to extract keywords，and co-word matrix of keywords and correlation and dissimilarity matrix are constructed；then Ucinet and SPSS are used to make cluster analysis，multi-dimensional scaling analysis and social network analysis of these keywords.In cluster analysis，the keywords of the popular science policy content are classified into 8 classes，which is respectively conducting training for youngsters and peasants，popular science resource sharing，infrastructure and fund input，etc. It shows that popular science policy covers a wide range of topics. In multidimensional scaling，popular science policy content is classified into 4 classes words， content of science popularization policy possess distinct focus points. By analyzing the keywords of the popular science policy content in network density，the study shows that there exist flaws in the content design of those policies. In a nutshell，the paper puts forward some suggestions which strengthen the hierarchy of policy content, and make policy content be more systematically conducted，etc.