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面向微博热点话题发现的改进BBTM模型研究

HUANG Chang GUO Wenzhong GUO Kun1,2,3+

计算机科学与探索2019,Vol.13Issue(7):1103-1114,12.
计算机科学与探索2019,Vol.13Issue(7):1103-1114,12.

面向微博热点话题发现的改进BBTM模型研究

Research on Improved BBTM Model for Microblog Hot Topic Discovery*

HUANG Chang 1GUO Wenzhong 2GUO Kun1,2,3+2

作者信息

  • 1. College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou 350116, China2. Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China3. Key Laboratory of Ministry of Education for Spatial Data Mining & Information Sharing, Fuzhou University, Fuzhou 350116, China
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摘要

Abstract

In order to overcome the problems of current hot topic discovery methods based on topic model, such as the sparsity of features, the high dimension, and the requirement for pre-specifying the number of topics, a hot topic discovery method based on an improved bursty biterm topic model (BBTM) which is called hot topic-hot biterm topic model (H-HBTM) is proposed. First, the word burst probability is used to select features and to filter the non-burst words. Second, the hot burst probability of micro-blog word pairs can be expressed by integrating the burst characteristic and the propagation characteristic of micro-blog texts. The hot burst probability is used as the prior probability of the BBTM model. Finally, a density based method is used to select the optimal number of topics for the BBTM model so that the optimal BBTM model is determined to detect hot topics. The experiments conducted on the real micro-blog datasets demonstrate that the H-HBTM can automatically find the optimal model without pre-specifying the number of topics, and the quality of the hot topics found is superior to the other methods, such as the BBTM, the biterm topic model and the latent Dirichlet allocation.

关键词

热点话题发现/微博/突发词对主题模型(BBTM)/主题模型

Key words

hot topic detection/ microblog/ bursty biterm topic model (BBTM)/ topic model

分类

信息技术与安全科学

引用本文复制引用

HUANG Chang,GUO Wenzhong,GUO Kun1,2,3+..面向微博热点话题发现的改进BBTM模型研究[J].计算机科学与探索,2019,13(7):1103-1114,12.

基金项目

The National Natural Science Foundation of China under Grant Nos. 61300104, 61300103, 61672158 (国家自然科学基金) (国家自然科学基金)

the High School Science Fund for Distinguished Young Scholars of Fujian Province under Grant No. JA12016 (福建省高校杰出青年科学基金) (福建省高校杰出青年科学基金)

the Program for New Century Excellent Talents in Fujian Province University under Grant No. JA13021 (福建省高等学校新世纪优秀人才支持计划) (福建省高等学校新世纪优秀人才支持计划)

the Natural Science Funds for Distinguished Young Scholars of Fujian Province under Grant Nos. 2014J06017, 2015J06014 (福建省杰出青年科学基金) (福建省杰出青年科学基金)

the Technology Innovation Platform Project of Fujian Province under Grant Nos. 2009J1007, 2014H2005 (福建省科技创新平台计划项目) (福建省科技创新平台计划项目)

the Natural Science Foundation of Fujian Province under Grant Nos. 2013J01230, 2014J01232 (福建省自然科学基金) (福建省自然科学基金)

the Industry-Academy Cooperation Project of Fujian Province under Grant Nos. 2014H6014, 2017H6008 (福建省高校产学合作项目). (福建省高校产学合作项目)

计算机科学与探索

OA北大核心CSCDCSTPCD

1673-9418

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