[1]倪丽萍,刘小军,马驰宇. 基于LDA模型和AP聚类的主题演化分析[J].计算机技术与发展,2016,26(12):6-11.
 NI Li-ping,LIU Xiao-jun,MA Chi-yu. Topic Evolution Analysis Based on LDA Model and AP Clustering[J].,2016,26(12):6-11.
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 基于LDA模型和AP聚类的主题演化分析()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年12期
页码:
6-11
栏目:
智能、算法、系统工程
出版日期:
2016-12-10

文章信息/Info

Title:
 Topic Evolution Analysis Based on LDA Model and AP Clustering
文章编号:
1673-629X(2016)12-0006-06
作者:
 倪丽萍刘小军马驰宇
 合肥工业大学 管理学院; 教育部过程优化与智能决策重点实验室
Author(s):
 NI Li-pingLIU Xiao-junMA Chi-yu
关键词:
 多主题演化时间窗口LDA模型AP聚类算法JS散度
Keywords:
 multiple topic evolutiontime windowLDA modelAP clustering algorithmJS distance
分类号:
TP181
文献标志码:
A
摘要:
 随着互联网的高速发展,网络信息呈现爆炸性增长态势,主题演化分析能够帮助人们从海量的互联网数据中获取更有价值的信息。分析主题的演化发展轨迹有利于人们了解主题事件发生的前因后果,并对主题事件发展趋势进行更好地预测,进而辅助管控。针对单个主题演化分析方法中阈值设定和主题漂移的问题,提出一种LDA-AP主题演化模型。该方法利用LDA模型对不同时间窗口内的新闻文本分别进行建模,得到相应的主题。利用AP聚类算法对不同时间窗口内的多个主题进行聚类,其中计算主题相似度采用加入时间衰减因子的JS散度来度量。最后对多个主题内容进行演化分析。通过相关的实验分析和对比,结果表明该方法可以改善主题演化的性能,并能较好地分析多个新闻主题事件随时间的演化趋势。
Abstract:
 With the rapid development of Internet,the network information presents explosive growth,and the topic evolution analysis can help people get more valuable information from the massive Internet data. Evolutionary trajectory analysis of the topic is helpful for peo-ple to understand the antecedents and consequences of the event and to better predict the development trend of theme events,assistance of control. Aiming at the problem of threshold setting and topic shift in the method of a single event evolution analysis,a new LDA-AP model is proposed. In this method,the LDA model is used to model the news texts in different time windows,and the topic of different time windows is obtained. Then the AP clustering algorithm is used to analyze the multiple topic in different time windows,in which topic similarity calculation using the JS divergence with attenuation factor to measure. Finally the evolution analysis of multiple topic is conduc-ted. Through experimental comparison with the reference method,the results show that the proposed method can effectively improve the performance of the topic evolution,and the evolution trend of multiple news events with time is better analyzed.

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更新日期/Last Update: 2017-02-03