Open AccessStudy of Sentiment Classification for Chinese Microblog Based on Recurrent Neural Network
Author(s) -
Zhang Yangsen,
Jiang Yuru,
Tong Yixuan
Publication year - 2016
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2016.07.002
Subject(s) - sentence , softmax function , computer science , artificial intelligence , microblogging , natural language processing , recurrent neural network , word (group theory) , classifier (uml) , sentiment analysis , social media , artificial neural network , mathematics , geometry , world wide web
The sentiment classification of Chinese Microblog is a meaningful topic. Many studies has been done based on the methods of rule and word‐bag, and to understand the structure information of a sentence will be the next target. We proposed a sentiment classification method based on Recurrent neural network (RNN). We adopted the technology of distributed word representation to construct a vector for each word in a sentence; then train sentence vectors with fixed dimension for different length sentences with RNN, so that the sentence vectors contain both word semantic features and word sequence features; at last use softmax regression classifier in the output layer to predict each sentence's sentiment orientation. Experiment results revealed that our method can understand the structure information of negative sentence and double negative sentence and achieve better accuracy. The way of calculating sentence vector can help to learn the deep structure of sentence and will be valuable for different research area.