面向主题的社交网络采集技术  

Topic Focused Crawling Technique on Social Network

作　　者：郑楷坚 沙灜 

机构地区：[1]中国科学院信息工程研究所,北京100093

出　　处：《计算机系统应用》2016年第10期173-179,共7页Computer Systems & Applications

基　　金：国家科技支撑计划(2012BAH46B03)

摘　　要：社交网络数据采集是开展社交网络分析的基础.针对当前面向主题的社交网络数据采集技术采集数据少、召回率低的问题,本文提出基于内置搜索引擎和基于通用搜索引擎相结合的主题消息采集方法,并将LDA(Latent Dirichlet Allocation,隐含狄利克雷分布)模型应用于主题关键词的迭代扩展,并提出了一种基于用户生存值的高效扩展策略.实验结果表明本文提出的方法可以使面向主题的社交网络数据采集系统在保证一定准确率的情况下进一步获取主题相关数据.Social network data is the basis of social network analysis that is why it＇s important to collect such data. To solve the problem of less collected data and low recall rate in current focused crawlers on social network, this paper proposes a method combining the based built-in search engine and general search engines to crawl topic messages, as well as applys the LDA model to extract the topic keywords from collected data iteratively and adds new topic keywords to the seed. Besides, an efficient expansion strategy based on users survival value is discussed. Our experiment shows that the methods proposed can improve the recall rate with a high precision.

关 键 词：社交网络 主题采集 内置搜索 通用搜索 主题模型 LDA 

分 类 号：TP274.2[自动化与计算机技术—检测技术与自动化装置]