An effective convolutional neural network model for Chinese sentiment analysis

Nowadays microblog is getting more and more popular. People are increasingly accustomed to expressing their opinions on Twitter, Facebook and Sina Weibo. Sentiment analysis of microblog has received significant attention, both in academia and in industry. So far, Chinese microblog exploration still...

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Bibliographic Details
Main Authors: Zhang, Yu, Chen, Mengdong, Liu, Lianzhong, Wang, Yadong
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Data mining
Embedding
Neural networks
Sentiment analysis
Online Access:Get full text
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Summary:Nowadays microblog is getting more and more popular. People are increasingly accustomed to expressing their opinions on Twitter, Facebook and Sina Weibo. Sentiment analysis of microblog has received significant attention, both in academia and in industry. So far, Chinese microblog exploration still needs lots of further work. In recent years CNN has also been used to deal with NLP tasks, and already achieved good results. However, these methods ignore the effective use of a large number of existing sentimental resources. For this purpose, we propose a Lexicon-based Sentiment Convolutional Neural Networks (LSCNN) model focus on Weibo’s sentiment analysis, which combines two CNNs, trained individually base on sentiment features and word embedding, at the fully connected hidden layer. The experimental results show that our model outperforms the CNN model only with word embedding features on microblog sentiment analysis task.
ISSN:0094-243X
1551-7616
DOI:10.1063/1.4982025