摘要
从科研文献数据中挖掘出学科前沿热点是目前学术界和工业界亟待解决的问题.社会网络可以及时反映信息传播的实际受欢迎程度,故提出一种基于社会网络关注度的学科前沿热点挖掘方法.首先通过数据相关性分析、相关属性划分以及社会网络关注度因子挖掘,构建文献热度评价模型.同时,采用文档主题生成模型(latent dirichlet allocation,LDA)从文献热度评价模型挖掘的科研文献中识别出该学科的前沿热点.最后,在"artificial intelligence and image processing"学科的数据集上构建评价模型并进行多组对比实验,结果表明提出的方法有效提高了学科热点挖掘结果的前沿性,热点主题在时间维度上更具时效性.
Mining subject hotspots from scientific literature data was an urgent problem in the academic and industry field. The existing methods couldn't consider the popularity of a scientific paper as a propagating object in the social network,which reflected the practical popularity of information diffusion timely. A hotspot mining method was proposed. Firstly,the literature popularity evaluation model was constructed for social network attention scoring. The model consisted of data correlation analyzing,correlated attributes partitioning and social network attention degree factor mining. Meanwhile,LDA model was adopted to discover the hotspots from the scientific papers. Finally,contrastive experiments were performed on the topic of artificial intelligence and image processing. The results showed that the method could timely discover recent hotspots,so it was effective in hot frontiers mining.
引文
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