Research on Text Classification Based on TextRank

DOI

10.2991/cimns-16.2016.79How to use a DOI?

Keywords

component; hadoop; TexTrank; naive bayes; text classification

Abstract

Extracting keywords from the result of word segmentation with the improved TextRank algorithm. Use the relative position of the words in the article to calculate the influence of position; the position of the coverage of the words and expressions is extended to the statement of the words and the key words as the feature of the text. Hadoop programming using naive Bayesian algorithm for text classification. The experiments show that the improved Textrank has a great improvement in classification performance, and the classification accuracy of naive Bayesian algorithm is 93% when the number of keywords is 40. Compared with the traditional, the accuracy rate increased by about 10%.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Guangming Lu
AU  - Yule Xia
AU  - Jiamei Wang
AU  - Zhenling Yang
PY  - 2016/09
DA  - 2016/09
TI  - Research on Text Classification Based on TextRank
BT  - Proceedings of the 2016 International Conference on Communications, Information Management and Network Security
PB  - Atlantis Press
SP  - 319
EP  - 322
SN  - 2352-538X
UR  - https://doi.org/10.2991/cimns-16.2016.79
DO  - 10.2991/cimns-16.2016.79
ID  - Lu2016/09
ER  -