基于社会网络分析与LDA的虚拟学术社区中用户群体主题挖掘研究

Theme Analysis of User Group in Virtual Academic Community Based on Social Network Analysis and LDA Model

【目的/意义】研究从用户群体的角度出发,依据用户特征对社区用户进行群体划分,以了解不同用户群体的主题差异,从而更加全面清晰的了解社区主题,更好的为社区用户推荐资源。【方法/过程】研究利用社会网络分析和Topsis算法对用户群体进行划分,再利用LDA模型分别对不同用户进行主题挖掘,最后采用谱聚类实现主题优化。【结果/结论】科学网情报学社区的核心用户与一般用户群体主题有相同的部分,也存在差异,核心用户群体的主题专指性较强,一般用户群体的主题较为广泛。基于虚拟学术社区用户群体主题挖掘模型,可以更加全面展示社区用户关注的主题,更好地为社区用户推荐资源。【创新/局限】研究从用户群体的视角出发,提出了虚拟学术社区用户群体主题挖掘模型,更好的为社区用户推荐资源,但本研究在数据量、主题模型以及社会网络分析指标的选取等方面还需要拓展与延伸。

【Purpose/significance】From the perspective of the user group,this study divides the community users into groups according to user characteristics to understand the topic differences of different user groups and the community topics more comprehensively and clearly.Thereby,recommending academic resources for community users better.【Method/process】Using social network analysis and Topics method to divide the user group,then use the LDA model to carry out topic mining for different users.Finally,we use spectral clustering to achieve topic optimization.【Result/conclusion】The core users of the Science Network Information Science community have the same parts as the topics of general user groups,and there are also differences.The topics of the core user groups are more specific and the topics of the general user groups are more extensive.Based on the virtual academic community user group topic mining model,the topics that community users care about can be displayed more comprehensively,and resources can be better recommended for community users.【Innovation/limitation】From the perspective of user groups,the study proposes a virtual academic community user group topic mining model to better recommend resources for community users.However,this study needs to be optimized in terms of data,topic model and selection of social network analysis indicators.