Structural information aware deep semi-supervised recurrent neural network for sentiment analysis
Structural information aware deep semi-supervised recurrent neural network for sentiment analysis作者机构：School of Computer Science and Engineering Beihang University Beijing 100191 China Research Institute of Beihang University in Shenzhen Shenzhen 518057 China
出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)
年 卷 期：2015年第9卷第2期
页 面：171-184页
核心收录：
学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：This work was partially supported by the Na- tional High Technology Research and Development Program of China (2011AA010502) the National Natural Science Foundation of China (Grant No. 61103095) and the Fundamental Research Funds for the Central Uni- versifies. We are grateful to Shenzhen Key Laboratory of Data Vitalization (Smart City) for supporting this research
主 题：sentiment analysis recurrent neural network,deep learning machine learning
摘 要：With the development of Internet, people are more likely to post and propagate opinions online. Sentiment analysis is then becoming an important challenge to under- stand the polarity beneath these comments. Currently a lot of approaches from natural language processing＇s perspec- tive have been employed to conduct this task. The widely used ones include bag-of-words and semantic oriented analy- sis methods. In this research, we further investigate the struc- tural information among words, phrases and sentences within the comments to conduct the sentiment analysis. The idea is inspired by the fact that the structural information is play- ing important role in identifying the overall statement＇s po- larity. As a result a novel sentiment analysis model is pro- posed based on recurrent neural network, which takes the par- tial document as input and then the next parts to predict the sentiment label distribution rather than the next word. The proposed method learns words representation simultaneously the sentiment distribution. Experimental studies have been conducted on commonly used datasets and the results have shown its promising potential.
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