Calculating the feature method of short text based on analytic hierarchy process

Xue-qiang ZOU; Xiu-guo BAO; Xiao-jun HUANG; Hong-yuan MA; Qing-sheng YUAN

doi:10.11959/j.issn.1000-436x.2016239

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Calculating the feature method of short text based on analytic hierarchy process

Academic paper | 更新时间：2024-06-05

- Calculating the feature method of short text based on analytic hierarchy process
- Journal on Communications Vol. 37, Issue 12, Pages: 50-55(2016)
- 作者机构：
  
  1. 中国科学院信息工程研究所，北京 100093
  2. 国家计算机网络应急技术处理协调中心，北京 100029
  3. 中国科学院大学，北京100049
  4. 北京邮电大学信息与通信工程学院，北京 100876
- 作者简介：
- 基金信息：
  
  The National High Technology Research and Development Program (863 Program)(SS2014AA012303);The National Natural Science Foundation of China(61300206);The National Natural Science Foundation of China(61402123)
- DOI：10.11959/j.issn.1000-436x.2016239
  CLC： TP391.1
- Online First：2016-12，
  
  Published：25 December 2016
- 稿件说明：
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Xue-qiang ZOU, Xiu-guo BAO, Xiao-jun HUANG, et al. Calculating the feature method of short text based on analytic hierarchy process[J]. Journal on Communications, 2016, 37(12): 50-55.
DOI：

Xue-qiang ZOU, Xiu-guo BAO, Xiao-jun HUANG, et al. Calculating the feature method of short text based on analytic hierarchy process[J]. Journal on Communications, 2016, 37(12): 50-55. DOI： 10.11959/j.issn.1000-436x.2016239.

摘要

为了建立用户精准兴趣模型以有效发现具有相似兴趣的用户群，提出了一种针对微博的短文本特征计算方法用于聚类算法，提升聚类效果以更好地挖掘微博用户的相似兴趣集合。该方法融合了微博转发数、评论数、点赞数等多个关键指标来度量微博短文本特征的重要性。同时，引入层次分析技术，改进了传统的tf-idf特征计算方法，并利用经典文本聚类算法进行实验。实验结果表明，改进后的短文本特征计算方法与传统的tf-idf特征计算方法相比，在类内集中度和类间分散度上取得了更好的效果。

Abstract

In order to model the accurate interest preference of microblog users and discover user groups with similar in-terest

a new method was proposed which considered the total amount of retweets

comments and attitudes of each mi-croblog for text feature calculation with utilizing classic analytical hierarchy process method. The proposed method used three indicators to evaluate the importance of the text feature representation and made an improvement on traditional tf-idf feature calculation method to fit for short text. Furthermore

this method was also implemented in the traditional clustering algorithm. Experimental results show that

compared with the traditional tf-idf method

the improved approach has a better clustering effect on the average scattering for clusters and the total separation between clusters.

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Keywords

references

AMR A , LIANG J H , ALEXANDER J S . Hierarchical geographical modeling of user locations from social media posts [C ] // The 22nd In-ternational Conference on World Wide Web , 2013 : 25 - 36 .

DAVID J . That's what friends are for: inferring location in online social media platforms based on social relationships [C ] // The 7th In-ternational Conference on Weblogs and Social Media . 2013 : 273 - 282 .

BOLLEGALA D , MATSUO Y , ISHIZUKA M . Measuring the similarity between implicit semantic relation using web search en-gines [C ] // The 2nd ACM International Conference on Web Search and Data Mining WSDM'09 , 2009 : 104 - 113 .

SUN A . Short text classification using very few words [C ] // The 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. NewYork, USA , 2012 : 1145 - 1146 .

RAMGE D , DUMAIS S , LIEBLINGI D . Characterizing microblogs with topic models [C ] // ICWSM , 2010 : 130 - 137 .

WENG J S , LIM E P , JIANG J , et al . TwitterRank: finding topic-sensitive influential Twitterers [C ] // The 3th ACM International Con-ference on Web Search and Data Mining. New York City , 2010 : 261 - 270 .

ABEL F , GAO Q , HOU B G J , et al . Semantic enrichment of twitter posts for user profile construction on the social Web [C ] // The 8th Ex-tended Semantic Web Conference on the Semanic Web: Research and Pages (ESWC'11) , 2011 : 375 - 389 .

WELCH M J , SCHONFELD U , HE D , et al . Topical semantics of Twitter links [C ] // The 4th ACM International Conference on Web Search and Data Mining (WSDM'11) . 2011 : 327 - 336 .

LIU Z , CHEN X , SUN M . Mining the interests of Chinese microblog-gers via keyword extraction [J ] . Frontiers of Computer Science in China , 2012 , 6 ( 1 ): 76 - 87 .

邱云飞，王琳颍，邵良杉，等．基于微博短文本的用户兴趣建模方法 [J ] ．计算机工程， 2014 , 40 ( 2 ): 275 - 279 .

QIU Y F , WANG L Y , SHAO L S , et al . User interest modeling ap-proach based on short text of micro-blog [J ] . Computer Engineering , 2014 , 40 ( 2 ): 275 - 279 .

宋巍，张宇，谢毓彬，等．基于微博分类的用户兴趣识别 [J ] ．智能计算机与应用， 2013 , 3 ( 4 ): 80 - 83 .

SONG W , ZHANG Y , XIE Y B , et al . Identifying user interests based on microblog classification [J ] . Intelligent Computer and Applications , 2013 , 3 ( 4 ): 80 - 83 .

方维．微博兴趣识别与推送系统的研究与实现 [D ] ．华中科技大学， 2012 .

FANG W . Research and implement of micro-blog interest found and pushing system [D ] . Huazhong University of Science and Technology , 2012 .

张俊林．标签传播算法在微博用户兴趣图谱的应用 [J ] ．程序员， 2012 , 1 ( 7 ): 50 - 53 .

ZHANG J L . Application of label propagation algorithm in user pro-files of micro-blog [J ] . Programmer , 2012 , 1 ( 7 ): 50 - 53 .

SALTON G , WONG A , YANG C S . A vector space model for auto-matic indexing [J ] . Communications of the ACM CACM Homepage , 1975 , 18 ( 11 ): 613 - 620 .

常建娥，蒋太立．层次分析法确定权重的研究 [J ] ．武汉理工大学学报（信息与管理工程版）， 2007 , 29 ( 1 ): 153 - 156 .

CHANG J E , JIANG T L . Research on determining weights by ana-lytic hierarchy process [J ] . Journal of Wuhan University of Technology (Information ＆ Management Engineering) , 2007 , 29 ( 1 ): 153 - 156 .

CHANG C C , LIN C J . LIBSVM: a library for support vector ma-chines [J ] . ACM Transactions on Intelligent Systems ＆ Technology , 2011 , 2 ( 3 ): 389 - 396 .

HALKIDI M , VAZIRGIANNIS M , BATISTAKIS Y . Quality scheme assessment in the clustering process [J ] . Lecture Notes in Computer Science , 2000 , 1910 ( 1 ): 265 - 276 .

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