CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
Parallel Recursive Deep Model for Sentiment Analysis
Li, Changliang1; Xu, Bo1; Wu, Gaowei1; He, Saike1; Tian, Guanhua1; Zhou, Yujun2
2015-05-19
Conference NameAnalysis,the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015)
Source PublicationAdvances in Knowledge Discovery and Data Mining
Conference Date2015-5-19 ~ 2015-5-22
Conference PlaceHo Chi Minh
AbstractSentiment analysis has now become a popular research problem to tackle in Artificial Intelligence (AI) and Natural Language Processing (NLP) field. We introduce a novel Parallel Recursive Deep Model (PRDM) for predicting sentiment label distributions. The main trait of our model is to not only use the composition units, i.e., the vector of word, phrase and sentiment label with them, but also exploit the information encoded among the structure of sentiment label, by introducing a sentiment Recursive Neural Network (sentiment-RNN) together with RNTN. The two parallel neural networks together compose of our novel deep model structure, in which Sentiment-RNN and RNTN cooperate with each other. On predicting sentiment label distributions task, our model outperforms previous state of the art approaches on both full sentences level and phrases level by a large margin.
KeywordSentiment Analysis Prdm Sentiment-rnn
DOI10.1007/978-3-319-18032-8 2
URL查看原文
Indexed ByEI
Language英语
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Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10778
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding AuthorLi, Changliang
Affiliation1.Institute of Automation Chinese Academy of Sciences
2.Jiangsu Jinling Science and Technology Group Co., Ltd
Recommended Citation
GB/T 7714
Li, Changliang,Xu, Bo,Wu, Gaowei,et al. Parallel Recursive Deep Model for Sentiment Analysis[C],2015.
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