Earticle

초록

영어

This article studied weibo text representation. For the weibo features such as short, real-time, colloquialism and originality, in the original vector space model, we propose a suitable method for weibo text representation. Make all the content words as feature words after participation. And we proposed T-TFIDF weight calculation method according to the features of weibo. According to the vector space model, we proposed a weibo adaptive topic tracking methods based on K-means clustering. Simulation analysis shows that, the method can by comparing the similarity micro-blog and sub topic vector set, determine whether weibo belonging to the topic.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Feature Weighting Algorithm
  2.2. K-means Clustering Algorithm
 3. Weibo Adaptive Topic Tracking Algorithm based on K-means
  3.1. An Improved Algorithm based on Feature Weighting
  3.2. Tracking Algorithm
  3.3. Abstract Topics
  3.4. Experiment and Result Analysis
 4. The Design and Realize of the Weibo Tracking System
  4.1. System Functions Overview
  4.2. System Overall Design
 5. Research Prospect
 References

키워드

저자

• Yun Liu [ School of Electronic and Information Engineering Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education Beijing Jiaotong University, Beijing, 100044, China ]
• Kun-Peng Xia [ School of Electronic and Information Engineering Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education Beijing Jiaotong University, Beijing, 100044, China ]
• Jian-Xun Zhao [ School of Electronic and Information Engineering Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education Beijing Jiaotong University, Beijing, 100044, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605