Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 417-422.

• Big Data & Data Mining • Previous Articles     Next Articles

Temporal Text Data Stream Feature Trend Model and Algorithm

MENG Zhi-qing, XU Wei-wei   

  1. School of Management,Zhejiang University of Technology,Hangzhou 310023,China
  • Online:2019-06-14 Published:2019-07-02

Abstract: Today,on the platform of e-commerce and social networking,there will be a lot of text data streams.It is very important to extract the characteristics of text data flow quickly to find some trend for guiding the operation of enterprises.For example,clothing enterprises must perceive popular information as quickly and accurately as possible.Fashion trends are of vital importance to the design,production and operation.Taken the text data flow of online goods as the research object,combining the online sales text real-time data flow,this paper defined a characteristic trend model of the temporal text data flow.Then,it proposed a real-time mining algorithm for text data stream feature trend finding.The algorithm was applied on the description of clothing sales text to extract popular feature applications.It can obtain an effective fashion trend and provide decision support for enterprises to formulate production plans and select marketing strategies.On the real sales data of the e-commerce platform,the experiment results prove that the algorithm has good accuracy and fast speed.Therefore,the proposed algorithm has important theoretical and practical significance.

Key words: Feature extraction, Real-time mining algorithm, Temporal text model, Text data stream

CLC Number: 

  • TP311
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