基于时间序列分析的微博突发话题检测方法

doi:10.11959/j.issn.1000-436x.2016052

Abstract

Abstract:

Detecting bursty topics from microblogs was an important task to understand the current events attracting a large number of internet users.However,the existing hods suitable for news articles cannot be adopted directly for microblogs.Because microblogs have unique characteristics compared wi formal texts,including diversity,dynamic and noise.A detection method for microblog bursty topic was proposed based on time series analysis,which was an op-timization method of momentum model.The candidate bursty features were extracted by momentum model.The time se-ries of feature's momentum were modled by frequency domain analysis theory and stock trend analysis theory.The fre-quently pseudo-bursty features were filtered according to analysis results of frequency-domain characteristics.The inter-mittently pseudo-bursty features were filtered according to the novelty analysis result through stock trend theory.The bursty topics were finally emerged with combination of effective bursty features.The experiments are conducted on a real Sina microblog data set.It show that the proposed method improves the precis and F-measure remarkably compared with the momentum modle.

Key words: bursty topic, microblog, bursty feature, time series analysis

Min HE,Jie2 XU,Pan1 DU,Xue-qi1 CHENG,Li-hong WANG. Bursty topic detection method for microblog based on time series analysis[J]. Journal on Communications, 2016, 37(3): 48-54.

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[1]	FUNG G , YU J , YU P , et al. Parameter free bursty events detection in text streams[C]// Conference on 31th VLDB. Trondheim,Norway, c2005:181-192.
[2]	HE Q , CHANG K , LIM E . Analyzing feature trajectories for event detection[C]// Conference on 30th SIGIR. Amsterdam, c2007:208-214.
[3]	KLEINBERG J . Bursty and hierarchical structure in steam[C]// Con-ference on KDD'02. Edmonton,Alberta,Canada, c2002:91-101.
[4]	CUI A , ZHANG M , LIU Y , et al. Discover breaking events with popu-lar hashtags in twitter[C]// Conference on CIKM'12. Maui,HI,USA, c2012:1796-1798.
[5]	DU Y Y , HE Y X , TIAN Y . Microblog bursty topic detection based on userrelationship[C]// 6th IEEE Information Technology and Artificial Intelligence Conference. Chongqing,China, c2011:260-263.
[6]	SHIVA P K , PREM M , ARINDAM B . Emerging topic detection using dictionary learning[C]// Conference on CIKM'11. Glasgow,Scotland,UK, c2011:745-754.
[7]	ZHU M L , HU W M , WU O . Topic detection and tracking for threaded discussion communities[C]// IEEE/WIC/ACM International Conferences on Web Intelligences and Intelligent Agent Technology. c2008:77-83.
[8]	贺敏，杜攀，张瑾，等. 基于有意义串动量模型的微博突发话题检测方法[J]. 计算机研究与发展， 2015,52(5): 1022-1028. HE M , DU P , ZHANG J , et al. Microblog bursty topic detection me-thod based on momentum model[J]. Journal of Computer h and Development, 2015,52(5): 1022-1028.
[9]	贺敏 . 面向互联网的有意义串挖掘[D]. 北京: 中国科学院计算技术研究所， 2007. HE M . Web-oriented Chinese meaningful string mining[D]. Beijing: Insti-tute of Computing Technology,Chinese Academy of Sciences, 2007.
[10]	ALAN R , MAUSAM O E . Open domain event extraction from twit-ter[C]// Conference on KDD'12. Beijing,China, c2012:1104-1112.
[11]	ANDREW J , YASHAR M , JOEMON M . Building a large-scale cor-pus for evaluating event detection on twitter[C]// Conference on CIKM'13. San Francisco,CA,USA, c2013:409-418.
[12]	DIAO Q M , JIANG J , ZHU F D , et al. Finding bursty topics from microblogs[C]// The 50th Annual Meeting of the Association for Computational Linguistics. Jeju,Korea, c2012:536-544.
[13]	POPESCU A M , PENNACCHIOTTI M , PARANJPE D A . Extracting events and event descriptions from twitter[C]// Conference on WWW'11. Hyderabad,India, c2011:105-106.
[14]	LI CL , SUN AX , DATTA A . Twevent:segment-based event detection from tweets[C]// Conference on CIKM'12. Maui,HI,USA, c2012:155-164.

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方法	P	R	F
TF-IDF＆UF-ITUF	70.27%	72.22%	71.23%
动量模型	87.71%	87.22%	87.47%
动量模型+频域分析	91.18%	86.11%	88.57%
动量模型+趋势分析	92.35%	87.22%	89.71%
动量模型+频域分析+趋势分析	96.27%	86.11%	90.91%

方法	识别数量	准确数量	准确率
动量模型	1 262	1 089	86.29%
动量模型+频域分析	1 024	933	91.11%
动量模型+趋势分析	1 185	1 087	91.73%
动量模型+频域分析+趋势分析	945	933	98.73%

Bursty topic detection method for microblog based on time series analysis

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