Journal of Zhejiang University

Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.1 P.49-55

http://doi.org/10.1631/jzus.2005.A0049

An improved TF-IDF approach for text classification*

Author(s): Yun-tao Zhang^1,2,Ling Gong²,Yong-cheng Wang²
Affiliation(s): 1. 1. Network & Information Center, Shanghai Jiaotong University, Shanghai 200030, China more
Corresponding email(s): ytzhang@mail.sjtu.edu.cn
Key Words: Term frequency/inverse document frequency (TF-IDF), Text classification, Confidence, Support, Characteristic words

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ZHANG Yun-tao, GONG Ling, WANG Yong-cheng. An improved TF-IDF approach for text classification[J]. Journal of Zhejiang University Science A, 2005, 6(1): 49-55.

@article{title="An improved TF-IDF approach for text classification",
author="ZHANG Yun-tao, GONG Ling, WANG Yong-cheng",
journal="Journal of Zhejiang University Science A",
volume="6",
number="1",
pages="49-55",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.A0049"
}

%0 Journal Article
%T An improved TF-IDF approach for text classification
%A ZHANG Yun-tao
%A GONG Ling
%A WANG Yong-cheng
%J Journal of Zhejiang University SCIENCE A
%V 6
%N 1
%P 49-55
%@ 1673-565X
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0049

TY - JOUR
T1 - An improved TF-IDF approach for text classification
A1 - ZHANG Yun-tao
A1 - GONG Ling
A1 - WANG Yong-cheng
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 1
SP - 49
EP - 55
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.A0049

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: This paper presents a new improved term frequency/inverse document frequency (TF-IDF) approach which uses confidence, support and characteristic words to enhance the recall and precision of text classification. Synonyms defined by a lexicon are processed in the improved TF-IDF approach. We detailedly discuss and analyze the relationship among confidence, recall and precision. The experiments based on science and technology gave promising results that the new TF-IDF approach improves the precision and recall of text classification compared with the conventional TF-IDF approach.