标题:An effective gated and attention-based neural network model for fine-grained financial target-dependent sentiment analysis
作者:Jiang M.[1];Wang J.[1];Lan M.[1,2];Wu Y.[1,2]
作者全称:Jiang, Mengxiao[1];Wang, Jianxiang[1];Lan, Man[1,2];Wu, Yuanbin[1,2]
通讯作者:Lan, Man(mlan@cs.ecnu.edu.cn)
通讯作者地址:Lan, M.; Department of Computer Science and Software Engineering, East China Normal UniversityChina; 电子邮件: mlan@cs.ecnu.edu.cn
出版年:2017
卷:10412 LNAI
页码:42-54
关键词:Attention neural network; Financial domain; Gate mechanism; Stock market prediction; Target-dependent sentiment analysis
摘要:In this work, we propose an effective neural network architecture GABi-LSTM to address fine-grained financial target-dependent sentiment analysis from 更多
收录类别:SCOPUS;EI
资源类型:外文期刊论文;外文会议论文
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