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2016 | OriginalPaper | Chapter

Chinese Sentiment Analysis Using Bidirectional LSTM with Word Embedding

Authors : Zheng Xiao, PiJun Liang

Published in: Cloud Computing and Security

Publisher: Springer International Publishing

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Abstract

Long Short-Term Memory network have been successfully applied to sequence modeling task and obtained great achievements. However, Chinese text contains richer syntactic and semantic information and has strong intrinsic dependency between words and phrases. In this paper, we propose Bidirectional Long Short-Term Memory (BLSTM) with word embedding for Chinese sentiment analysis. BLSTM can learn past and future information and capture stronger dependency relationship. Word embedding mainly extract words’ feature from raw characters input and carry important syntactic and semantic information. Experimental results show that our model achieves 91.46 % accuracy for sentiment analysis task.

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Metadata
Title
Chinese Sentiment Analysis Using Bidirectional LSTM with Word Embedding
Authors
Zheng Xiao
PiJun Liang
Copyright Year
2016
Book
Cloud Computing and Security

Print ISBN: 978-3-319-48673-4

Electronic ISBN: 978-3-319-48674-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-48674-1_53

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