On hot topic detection by merging temporal and volatility

doi:10.3969/j.issn.1000-1565.2018.04.012

Abstract

Abstract: Temporal and volatility are directly related to the hot degree of topic, namely, the more related stories the topic has in a certain time distance, and the larger fluctuation the topic is, the higher hot degree of this topic. Applying integration theory, methods of computing the density of related stories and the peak value are put forward based on temporal and volatility respectively. Finally, linear meditated method is used to merge them to compute the hot degree of a topic. Experiments are carried out on TDT4 corpus to testify the validity and rationality of our new method.

Key words: hot topic, detection, temporal, volatility

CLC Number:

TP390

LI Hancai,XU Jianmin,WU Shufang. On hot topic detection by merging temporal and volatility[J]. Journal of Hebei University (Natural Science Edition), 2018, 38(4): 416-422.

References

[1] LI H, WEI J. Netnews bursty hot topic detection based on bursty features [Z]. International Conference on E-Business and E-Government, Washington DC, USA, 2010. DOI: 10.1109/ICEE.2010.365.
[2] XU G, QIU L, LIU H. Study on hot topic discovery from Chinese Texts [J]. International Journal of Information Management, 2014, 12(4): 267-273.
[3] 薛峰, 周亚东. 一种突发性热点话题在线发现与跟踪方法 [J]. 西安交通大学学报, 2011, 12(45): 64-70. DOI:10.7652/xjtuxb201112012. XUE F, ZHOU Y D. An online detection and tracking method for busty topics [J]. Journal of Xi’an Jiao Tong University, 2011, 12(45): 64-70. DOI:10.7652/xjtuxb 201112012.
[4] 陈羽中, 方明月, 郭文忠. 面向微博热点话题发现的多标签传播聚类方法研究 [J]. 模式识别与人工智能, 2015, 28(1): 1-10.DOI:10.16451/j.cnki.issn1003-6059.201501001. CHEN Y Z, FANG M Y, GUO W Z. Research on multi-lable propagation clustering method for microblog hot topic detection [J]. Pattern Recognition and Artificial Intelligence. 2015, 28(1): 1-10.DOI:10.16451/j.cnki.issn1003-6059.201501001.
[5] 魏德志, 陈福集, 林丽娜. 一种基于时间序列的热点话题发现模型和算法[J]. 情报科学. 情报科学, 2017, 10: 142-146. DOI: 10.13833/j.cnki.is.2017.10.026. WEI D Z, CHEN F J, LIN L N. A hot topic detection model and tracking algorithm based on time-series [J]. Information Science, 2017, 10: 142-146. DOI: 10.13833/j.cnki. is.2017.10.026.
[6] 孙明溪, 刘春琦. 基于DBSCAN算法与句间关系的热点话题发现研究[J].图书情报工作, 2017, 61(12): 113-121. DOI: 10.13266/j.issn.0252-3116.2017.12.015.
[7] 饶洁, 林育曼, 陈海媚. 基于粒子群算法的微博热点话题发现分析 [J]. 情报科学, 2016, 34(12): 51-54. DOI: 10.13833/j.cnki.is.2016.12.010. RAO J, LIN Y M, CHEN H M. Microblog hot topics tracking method based on particles swarm optimization [J]. Information Science, 2016, 34(12): 51-54. DOI: 10.13833/j.cnki.is.2016.12.010.
[8] 伍万坤, 吴清烈, 顾锦江. 基于EM-LDA综合模型的电商微博热点话题发现 [J]. 现代图书情报技术, 2015, 31(11): 33-40. DOI: 10.11925/infotech.1003-3513. 2015.11.06. WU W K, WU Q L, GU J J. Hot topic extraction from e-commerce microblog based on EM-LDA integrated model [J]. New Technology of Library and Information Service. 2015, 31(11): 33-40. DOI: 10.11925/infotech.1003-3513. 2015.11.06.
[9] 徐佳俊, 杨飏, 姚天昉,等. 基于LDA模型的论坛热点话题识别和追踪 [J]. 中文信息学报, 2016, 30(01): 43-49. XU J J, YANG Y, YAO T F, et al. LDA based hot topic detection and tracking for the forum [J]. Journal of Chinese Information Processing, 2016, 30(01): 43-49.
[10] 洪宇, 仓玉, 姚建民. 话题跟踪中静态和动态话题模型的核捕捉衰减[J]. 软件学报, 2012, 23(5):1100-1119. HONG Y, CANG Y, YAO J M. Descending kernel track of static and dynamic topic models in topic tracking [J]. Journal of Software,2012, 23(5):1100-1119.
[11] FUNG P C, YU J X, LIU H, et al. Time-dependent event hierarchy construction [Z]. The 13th ACM SIGKDD, New York, ACM, 2007. DOI: 10.1145/1281192.1281227.
[12] 叶雷, 高盛祥, 余正涛,等. 基于事件元素无向图的查询扩展方法 [J]. 中文信息学报, 2017, 31(1): 17-22. YE L, GAO S Y, YU Z T, et al. A query expansion method based on undirected graph of event elements [J]. Journal of Chinese Information Processing, 2017, 31(1): 17-22.
[13] ALLAN J, PAAPKA R, LAVRENKO V. On-line new event detection and tracking [Z]. The 21st Annual International ACM SIGIR Conference, New York: ACM, 1998.DOI: 10.1145/290941.290954.
[14] YANG Y, PIERCE T, CARBONELL J. A study on retrospective and on-line event detection [Z]. The 21st Annual International ACM SIGIR Conference, New York, ACM, 1998.
[15] CUTTING D R, KARGER D R, PEDERSEN J O, et al. Scatter/gather: a cluster-based approach to browsing large document collections [Z]. The 15th Annual International ACM SIGIR Conference, New York, ACM, 1992.
[16] 卢美莲, 王梓, 李佳珊. 利用改进LSH算法进行层次化新闻话题检测 [J]. 北京邮电大学学报, 2014, 37(3): 32-37. DOI: 10.13190/j.jbupt.2014.03.007. LU M L, WANG Z, LI J S. Hierarchical news topic detection using improved LSH [J]. Journal of Beijing University of Posts and Telecommunication, 2014, 37(3): 32-37. DOI: 10.13190/j.jbupt.2014.03.007.
[17] STEWART J. 微积分 [M]. 北京:高等教育出版社, 2014.
[18] 钱云. 非均衡数据分类算法若干应用研究 [D]. 长春: 吉林大学,2014. QIAN Y. Research on application of classification algorithm for imbalanced data [D]. Changchun: Jilin University,2014.
[19] GOUTTE C, GAUSSIER E. A probabilistic interpretation of precision, recall and F-score, with implication for evaluation [J]. International Journal of Radiation Biology, 2005, 51(5): 199-239. DOI: 10.1007/978-3-540-31865-1_25.