Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)

Attention-based ResNet for Chinese Text Sentiment Classification

Authors
Hu Han, Xuxu Bai, Jin Liu
Corresponding Author
Hu Han
Available Online February 2018.
DOI
https://doi.org/10.2991/csece-18.2018.108How to use a DOI?
Keywords
sentiment classification; attention mechanism; ResNet
Abstract
Identifying sentiment polarity of a document is a building block of sentiment classification and natural language processing tasks, it aims to automate the prediction of sentiment orientation in a document. In general, recently fast-growing Deep Neural Networks(DNN) method has been extensively used as a sentiment learning approach. But the dominant approach for sentiment classification tasks are recurrent neural networks, in particular LSTM, and convolutional neural networks. However, these architectures are rather shallow in comparison to the Residual Neural Networks(ResNet) which have pushed in computer vision. We present a model using ResNet for high-level document representation, and attention mechanism to capture the crucial components for document. The experimental results show that using up to 2 ResNet block and attention mechanism achieve state-of-the-art performance on three public sentiment classification datasets.
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Proceedings
2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)
Part of series
Advances in Computer Science Research
Publication Date
February 2018
ISBN
978-94-6252-487-3
ISSN
2352-538X
DOI
https://doi.org/10.2991/csece-18.2018.108How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Hu Han
AU  - Xuxu Bai
AU  - Jin Liu
PY  - 2018/02
DA  - 2018/02
TI  - Attention-based ResNet for Chinese Text Sentiment Classification
BT  - 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/csece-18.2018.108
DO  - https://doi.org/10.2991/csece-18.2018.108
ID  - Han2018/02
ER  -