[Purpose/significance] The microblog commentary sentiment classification model can play a guiding role for the relevant public opinion supervision departments to correctly control the development of the topic events and the public opinion.[Method/process] Based on the multi-scale convolutional neural network of word vector, this paper used multi-scale convolution kernel to improve the conditional constraints of finite context information in microblog commentary, and constructed multi-scale convolutional neural network microblog commentary emotion classification model based on word vector. Finally, the feasibility and superiority of the model were verified by crawling the real data of "microblogging hot search and rectification".[Result/conclusion] Verification results show that the multi-scale convolutional neural network based on word vector performs well in the short text classification task with limited context information such as weibo public opinion. On the theoretical level, this paper provides a more accurate emotional classification theory model and classification method for microblogging public opinion emotion classification. In practice, it can better guide the public opinion supervision department to better guide and supervise the emotional sentiment of public opinion.
Zhang Liu
,
Wang Xiwei
,
Huang Bo
,
Liu Yutong
. A Sentiment Classification Model and Experimental Study of Microblog Commentary Based on Multivariate Convolutional Neural Networks Based on Word Vector[J]. Library and Information Service, 2019
, 63(18)
: 99
-108
.
DOI: 10.13266/j.issn.0252-3116.2019.18.012
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