基于深度学习的微博情感分析

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Abstract Chinese micro-blog sentiment analysis aims to discover the user attitude towards hot events. Most of the current studies analyze the micro-blog sentiment by traditional algorithms such as SVM, CRF based on hand-engineered features. This paper explores the feasibility of performing Chinese micro-blog sentiment analysis by deep learning. We try to avoid task-specific features, and use recursive neural networks to discover relevant features to the tasks. We propose a novel model - sentiment polarity transition model - based on the relationship between neighboring words of a sentence to strengthen the text association. The proposed method achieves a performance close to state-of-the-art methods based on the hand-engineered features, but saving a lot of manual annotation work.

Key words： deep learning micro-blog sentiment analysis recursive neural networks autoencoder

Received: 14 June 2014

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	Articles by authors
	LIANG Jun
	CHAI Yumei
	YUAN Huibin
	ZAN Hongying
	LIU Ming

Cite this article:

LIANG Jun,CHAI Yumei,YUAN Huibin, et al. Deep Learning for Chinese Micro-blog Sentiment Analysis[J]. , 2014, 28(5): 155-161.

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http://jcip.cipsc.org.cn/EN/ OR http://jcip.cipsc.org.cn/EN/Y2014/V28/I5/155

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