基于半监督的短语情感倾向性分析方法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201604001

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Abstract The existing methods for sentiment analysis can not dig more complex linguistic phenomena in emotional expression and they encounter the challenge of sparse features. An innovative semi-supervised phrase-level sentiment analysis method based on semantic space model is proposed. Firstly, the problem of word representation in semantic space is discussed and word-level semantic distribution computing methods based on dependency grammar semantic space model are proposed, and the computational procedure is completed by using unsupervised method. Secondly, the problems of phrase recognition and representation are discussed and nonlinear combinations of word-level semantic distribution are used to represent the multi-word structures. Finally, a neutral network algorithm is used to design the phrase-level sentiment analysis system based on word level semantic distribution and phrasal structure representation. Experimental results on real Chinese corpora show the expected recognition accuracy of the model.

Key words： Sentiment Analysis Word Representation Phrase-Level Composition Neural Network

Received: 10 September 2015

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China (No.61472393)

About author:: (Odbal(Corresponding author)， born in 1981， master， assistant professor. Her research interests include natural language processing， affective computing， artificial intelligence and pa-ttern recognition.)
(WANG Zengfu， born in 1960， Ph.D.， professor. His research interests include stereo vision， biometrics， affective computing and intelligent robot.)

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Odbal,WANG Zengfu. A Semi-supervised Method for Phrase-Level Sentiment Analysis[J]. , 2016, 29(4): 289-297.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201604001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I4/289

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