A multidimension document representation
approach based on social features

CHEN Gong1,HUANG Ruizhang1,2,ZHONG Wenliang1

（1.College of Computer Science and Technology,Guizhou University,Guiyang 550025;

2.Guizhou Provincial Key Laboratory of Public Big Data,Guiyang 550025,China）

Received:2016-07-03 Revised:2016-09-01 Online:2016-11-25 Published:2016-11-25

Abstract:

For all web document analysis approaches, finding good representation of web documents

plays a fundamental role and greatly affects the performance of web document analysis. We

propose a multidimension representation scheme for web documents. In addition to

extracting features directly from document contents, which is normally employed by

tradition document representation approaches, we also represent web documents with deeper

features that can be learned internally from documents and externally from web document

contexts. We exploit the three representation dimensions, including superficial dimension,

latent dimension and social dimension, extract and discover the features of superficial and

latent dimensions internally from document contents, and capture the social dimension

features externally from the interaction behavior between users and web documents. The

proposed multidimension representation scheme can be applied to document analysis models.

We conduct extensive experiments to evaluate its effectiveness in terms of document

clustering performance. Two common document clustering algorithms, multidimension k

means and multidimension hierarchical agglomerative clustering, are investigated.

Experiments verify that the proposed multidimension document representation scheme is

effective. Moreover, we report interesting observations in crossdimension features

discovered from experimental results.

Key words: document representation, document clustering, social feature

CHEN Gong1,HUANG Ruizhang1,2,ZHONG Wenliang1.

A multidimension document representation

approach based on social features

[J]. Computer Engineering & Science.

[1]	MA Yongjun,LI Yajun,WANG Rui,CHEN Haishan. A document representation model based on Wasserstein GAN [J]. Computer Engineering & Science, 2019, 41(01): 161-165.
[2]	CHEN Weimin1，2，CHEN Zhigang2,CUI Fang2，LIU Jiaqi2. A packet transmission mechanism based on hybrid social groups in mobile opportunistic networks [J]. Computer Engineering & Science, 2016, 38(11): 2202-2208.
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