Computer Science ›› 2019, Vol. 46 ›› Issue (12): 69-73.doi: 10.11896/jsjkx.190400107

• Big Data & Data Science • Previous Articles     Next Articles

Short Text Feature Expansion and Classification Based on Non-negative Matrix Factorization

HUANG Meng-ting, ZHANG Ling, JIANG Wen-chao   

  1. School of Computers,Guangdong University of Technology,Guangzhou 510006,China
  • Received:2019-04-18 Online:2019-12-15 Published:2019-12-17

Abstract: In this paper,a feature extension method based on non-negative matrix factorization (NMFFE) was proposed to overcome the sparse of short text feature.This method only considers the data itself and does not rely on external resources for feature extension.Firstly,the internal relationship of text and word is taken into account in the factorization of the relationship matrix between text and word ,and word clustering instruction matrix is obtained by graph dual re-gularization non-negative matrix triple factorization (DNMTF) method.Then,word clustering instruction matrix is reduced in dimensionality to get the feature space.Finally,according to the degree of correlation between words,the feature in the feature space is added to the short text,thus solving the problem of feature sparse in short text and improving the accuracy of text classification.The experimental data show that compared with the better performance in BOW algorithm and Char-CNN algorithm,the accuracy of short text classification based on NMFFE algorithm is increased by 25.77%,10.89% and 1.79% on the three datasets,which are Web snippets,Twitter sports and AGnews,respectively.The experimental data fully demonstrate that NMFFE algorithm is superior to BOW algorithm and Char-CNN algorithm in terms of classification accuracy and algorithm robustness.

Key words: Short text classification, Feature extension, Non-negative matrix factorization, Feature space, Correlation

CLC Number: 

  • TP391
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