Analysis on topic evolution of news comments by
combining word vector and clustering algorithm

Computer Engineering & Science

Analysis on topic evolution of news comments by

combining word vector and clustering algorithm

LIN Jianghao1，ZHOU Yongmei1，2,YANG Aimin1，2,WANG Wei2

（1.Laboratory for Language Engineering and Computing,Guangdong University of Foreign

Studies,Guangzhou 510006;

2.Cisco School of Informatics,Guangdong University of Foreign Studies,Guangzhou

510006,China）

Received:2016-07-01 Revised:2016-09-05 Online:2016-11-25 Published:2016-11-25

Abstract

Abstract:

The analysis of topic evolution is regarded as the mining of topic content evolving with

the time. This article, based on the hypothesis that topic content may be embodied by key

words, adopt word2vec for the training of 750 thousand pieces of news and microblog texts

to establish the model of word vector. The text information flow is applied to the model

and all word vectors by time series are acquired. Kmeans is used to cluster the word

vectors before the key words are drawn and the analysis of topic evolution is visualized.

By comparing the effect of the word vector model with those of PLSA or LDA topic models on

drawing topic, the results show that the former is more effective than the latter two

models. In addition, the collection of abundant and varied data can facilitate the training

of the word vector model with better generalization ability and the investigation on real

time analysis of topic evolution.

Key words: topic evolution, word2vec, PLSA, LDA

LIN Jianghao1，ZHOU Yongmei1，2,YANG Aimin1，2,WANG Wei2.

Analysis on topic evolution of news comments by

combining word vector and clustering algorithm

[J]. Computer Engineering & Science.

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Analysis on topic evolution of news comments by

combining word vector and clustering algorithm

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