大数据 ›› 2020, Vol. 6 ›› Issue (5): 82-91.doi: 10.11959/j.issn.2096-0271.2020045

• 研究 • 上一篇    

基于分层注意力网络的方面情感分析

宋婷1,陈战伟2,杨海峰1   

  1. 1 太原科技大学计算机科学与技术学院,山西 太原 030024
    2 中国移动通信集团山西有限公司,山西 太原 030001
  • 出版日期:2020-09-20 发布日期:2020-09-29
  • 作者简介:宋婷(1984- ),女,太原科技大学计算机科学与技术学院中级实验师,主要研究方向为人工智能与数据挖掘|陈战伟(1984- ),男,中国移动通信集团山西有限公司高级工程师,主要研究方向为人工智能与数据挖掘|杨海峰(1980- ),男,博士,太原科技大学计算机科学与技术学院教授,主要研究方向为人工智能与数据挖掘

Aspect sentiment analysis based on a hierarchical attention network

Ting SONG1,Zhanwei CHEN2,Haifeng YANG1   

  1. 1 College of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan 030024,China
    2 China Mobile Communications Group Shanxi Co.,Ltd.,Taiyuan 030001,China
  • Online:2020-09-20 Published:2020-09-29

摘要:

基于深度学习的方面情感分析是自然语言处理的热点之一。针对方面情感,提出基于方面情感分析的深度分层注意力网络模型。该模型通过区域卷积神经网络保留文本局部特征和不同句子时序关系,利用改进的分层长短期记忆网络(LSTM)获取句子内部和句子间的情感特征。其中,针对LSTM添加了特定方面信息,并设计了一个动态控制链,改进了传统的LSTM。在SemEval 2014的两个数据集和Twitter数据集上进行对比实验得出,相比传统模型,提出的模型的情感分类准确率提高了3%左右。

关键词: 深度学习, 方面情感, 区域卷积神经网络, 分层长短期记忆网络, 注意力机制, 动态控制链

Abstract:

Aspect sentiment analysis based on deep learning is one of the hot spots in natural language processing.Aiming at aspect sentiment,a deep hierarchical attention network model based on aspect sentiment analysis was proposed.The local features of the text and the temporal relationship of different sentences were retained in model through the convolutional neural network,and the emotional features within and between sentences were obtained by using the layered long shortterm memory network (LSTM).Among them,specific aspects of information were added to LSTM and a dynamic control chain was designed to improve the traditional LSTM.A comparative experiment is conducted on the two data sets in SemEval 2014 and the Twitter data set.Compared with the traditional model,the accuracy of sentiment classification of the proposed model increases by about 3%.

Key words: deep learning, aspect sentiment, regional convolutional neural network, layered long short-term memory, attention mechanism, dynamic control chain

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