Study of Sentiment Classification for Chinese Microblog Based on Recurrent Neural Network

ZHANG Yangsen; JIANG Yuru; TONG Yixuan

doi:10.1049/cje.2016.07.002

Volume 25 Issue 4

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ZHANG Yangsen, JIANG Yuru, TONG Yixuan, “Study of Sentiment Classification for Chinese Microblog Based on Recurrent Neural Network,” Chinese Journal of Electronics, vol. 25, no. 4, pp. 601-607, 2016, doi: 10.1049/cje.2016.07.002

Citation:

ZHANG Yangsen, JIANG Yuru, TONG Yixuan, “Study of Sentiment Classification for Chinese Microblog Based on Recurrent Neural Network,” Chinese Journal of Electronics, vol. 25, no. 4, pp. 601-607, 2016, doi: 10.1049/cje.2016.07.002

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Study of Sentiment Classification for Chinese Microblog Based on Recurrent Neural Network

doi: 10.1049/cje.2016.07.002

Institute of Intelligent Information Processing, Beijing Information Science and Technology University, Beijing 100192, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61370139), The Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges Under Beijing Municipality (No.IDHT20130519), and Scientific Research Project of Beijing Municipal Commission of Education (No.KM201411232014).

Received Date: 2015-11-01
Rev Recd Date: 2016-02-10
Publish Date: 2016-07-10

Abstract

Abstract

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.
- Recurrent neural network (RNN),
- Distributed representation,
- Sentiment classification,
- Sentence vector

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References(18)

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