Topic Detection of Chinese News Based on Word Entropy

DOI

10.2991/icmit-16.2016.55How to use a DOI?

Keywords

Word entropy; topic detection; topic word co-occurrence net; modularity measure

Abstract

We propose a method of automatic news topic detection in large-scale data. First, topic words are detected based on their word entropy. Then, the topic word co-occurrence net is constructed via the semantic relationships of topic words represented by their orders in which they appear within the original text. Finally, implied communities are detected in the topic word co-occurrence net through modularity measures. Each implied community is regarded as a news topic. Experimental results show that this method can be used to effectively identify the key topic of each news report, with the presence of topic content in human-readable form.

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/).

Download article (PDF)

TY  - CONF
AU  - Bo Zhu
AU  - Min Hou
AU  - Yuyin He
PY  - 2016/04
DA  - 2016/04
TI  - Topic Detection of Chinese News Based on Word Entropy
BT  - Proceedings of the 2016 3rd International Conference on Mechatronics and Information Technology
PB  - Atlantis Press
SP  - 313
EP  - 322
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmit-16.2016.55
DO  - 10.2991/icmit-16.2016.55
ID  - Zhu2016/04
ER  -