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一种适用于微博主题提取的SMLDA模型

An SMLDA model of topic extraction for micro-blog
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摘要 针对微博文本简短、格式内容散乱、信息描述不完全、数据噪声大导致无法高效获取微博主题的问题,提出一种基于LDA改进的SMLDA模型。该模型综合考虑微博作者之间的关系、微博特定主题的标签以及微博文本之间转发关系和背景主题,采用Gibbs抽样算法推导模型参数。在真实新浪微博数据集上进行试验分析,实验结果表明,SMLDA模型与LDA模型比较,前者效率更高,提取结果更准确。 Due to the short message,scattering format and content,incomplete description and data noise,the micro-blog topic can not be obtained efficiently.An improved SMLDA model based on LDA is proposed.The model mainly takes mi-cro-blog authors association,the specific topic tags,the relay document association and the background topic into considera-tion and adopts the Gibbs sampling algorithm to derive parameters.The experimental results on Sina micro-blog data set show that compared with LDA model,the SMLDA model is more effective.
作者 华绿绿 黄廷磊 刘久云 夏威
机构地区 桂林电子科技大学计算机科学与工程学院
出处 《桂林电子科技大学学报》 2015年第3期241-244,共4页 Journal of Guilin University of Electronic Technology
基金 国家863计划(2012AA011005)
关键词 新浪微博 Gibbs算法 主题提取 Sina micro-blog Gibbs algorithm topic extraction
分类号 TP393 [自动化与计算机技术—计算机应用技术]
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参考文献13

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二级参考文献39

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桂林电子科技大学学报
2015年 第3期

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