NDLI: Attention-based Hierarchical LSTM Model for Document Sentiment Classification

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Attention-based Hierarchical LSTM Model for Document Sentiment Classification

Content Provider	Scilit
Author	Wang, Bo Fan, Binwen
Copyright Year	2018
Description	Journal: Iop Conference Series: Materials Science and Engineering Document sentiment classification is a fundamental task in data mining, contains extensive underlying commercial value. With the development of deep learning, we can extract features in an automatic way, instead of design it by oneself. Which can help us use semantic information to classify the document in a better way. Base that, in this paper, we present a hierarchical network structure according to the structure in real document. Based on LSTM to encode semantic information; then combine with attention mechanism to improve the accuracy of classification. And last, conduct experiment on two dataset, analyse the accuracy result of different model, and study some tricks in parameter selection.
Related Links	http://iopscience.iop.org/article/10.1088/1757-899X/435/1/012051/pdf
ISSN	17578981
e-ISSN	1757899X
DOI	10.1088/1757-899x/435/1/012051
Journal	Iop Conference Series: Materials Science and Engineering
Issue Number	1
Volume Number	435
Language	English
Publisher	IOP Publishing
Publisher Date	2018-11-05
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Materials Science and Engineering Artificial Intelligence Sentiment Classification Semantic Information Classification Document Sentiment
Content Type	Text
Resource Type	Article

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