基于时空主题模型的微博主题提取

段炼; 呙维; 朱欣焰; 胡宝清

doi:10.13203/j.whugis20120604

基于时空主题模型的微博主题提取

段炼^1,2,3,
呙维^1,,
朱欣焰⁴,
胡宝清^2,3

¹ 武汉大学测绘遥感信息工程国家重点实验室,湖北武汉,430079;² 广西师范学院北部湾环境演变与资源利用教育部重点实验室,广西南宁,530001;³ 广西师范学院资源环境科学学院,广西南宁,530001;⁴ 武汉大学空天信息安全与可信计算教育部重点实验室,湖北武汉,430079

基金项目: 国家863计划资助项目(2013AA12A203,2011AA010502)；国家科技支撑计划资助项目(2012BAH35B03)；广西北部湾重大基础研究专项基金资助项目(2011GXNSFE018003,2012GXNSFEA053001)

详细信息

作者简介:
段炼,博士生,讲师,现从事智能空间信息服务研究。

通讯作者:
呙维

中图分类号: P208
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出版历程
- 收稿日期: 2013-05-14
- 修回日期: 2014-02-04
- 发布日期: 2014-02-04

Constructing Spatio-Temporal Topic Model for Microblog Topic Retrieving

DUAN Lian^1,2,3,
GUO Wei^1,,
ZHU Xinyan⁴,
HU Baoqing^2,3

¹State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China;²Education Ministry Key Laboratory of Environment Evolution and Resources Utilization in Beibu Bay,Nanning 53001,China;³Resources and Environment Science Department,Guangxi Teachers Education University,Nanning 530001,China;⁴Key Laboratory of Aerospace Information Security and Trusted Computing of Minstry of Education,Wuhan University,Wuhan 430079,China

More Information

Author Bio:
DUAN Lian,PhD candidate,specializes in intelligent spatial information service.

Corresponding author:
GUO Wei

摘要

摘要: 目的已有地理主题模型没有考虑不同区域对微博主题影响程度的差异性,同时他们将时间要素离散化,难以得到连续时间上的微博主题强度。提出了一种顾及连续时间及区域影响力因素的时空主题模型。该方法将城市划分为多个区域,依据各兴趣点类型及数量对区域赋予权重以表达区域社会功能对微博主题的影响程度,基于稀疏增量式生成模型表达微博主题分布,利用 Beta分布描述主题在连续时间中的强度,最终通过Gibbs采样得到时空主题模型各参数。实验表明,本文方法能发现连续时间上微博主题的演变,与已有地理主题模型相比,能更加准确地提取微博主题。
- 地理主题模型 /
- 微博主题挖掘 /
- 时空分布 /
- 时空推理
Abstract: Objective Existing geography topic models do not consider the degree to which different regions influ-ence microblog topics.Meanwhile,these models describe the topic evolutions in a discrete mannerwhich prevents the acquisition of topic intensities over continuous time.This paper proposes a novelspatio-temporal topic model to discover microblog topics by introducing continuous time and region in-fluences.A city was divided into multiple geographic regions.Region weights,expressing the regionfunction influence degree on microblog topics,were allocated to regions based on the number of differ-ent POI(Point of Interest)types.Then a sparse additive generative model was applied to generate mi-croblog topic distributions.Beta distributions were employed to depict topic evolution over continuoustime.Finally,we use a Gibbs sampling method to estimate model parameters.Experimental resultsshowed that not only does our model track the temporal distribution of microblog topics but also en-hances topic extraction accuracy when compared with other geography topic models.
- geography topic model /
- microblog topic mining /
- temporal-spatio distribution /
- temporal-spatio inference

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Jianjun ZHU，Leyang WANG，Jun HU，Bofeng LI，Haiqiang FU，Yibin YAO. Recent Advances in the Geodesy Data Processing. Journal of Geodesy and Geoinformation Science. 2023(03): 33-45 .

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