基于分层注意力网络的方面情感分析

宋婷; 陈战伟; 杨海峰

doi:10.11959/j.issn.2096-0271.2020045

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基于分层注意力网络的方面情感分析

研究 | 更新时间：2024-06-03

- 基于分层注意力网络的方面情感分析
- Aspect sentiment analysis based on a hierarchical attention network
- 大数据 2020年6卷第5期页码：2020045-1
- 作者机构：
  
  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
- 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" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2020045
  中图分类号： TP391
- 网络首发：2020-09，
  
  纸质出版：2020-09-15
- 稿件说明：
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宋婷, 陈战伟, 杨海峰. 基于分层注意力网络的方面情感分析[J]. 大数据, 2020,6(5):2020045-1.

Ting SONG, Zhanwei CHEN, Haifeng YANG. Aspect sentiment analysis based on a hierarchical attention network[J]. Big Data Research, 2020, 6(5): 2020045-1.
宋婷, 陈战伟, 杨海峰. 基于分层注意力网络的方面情感分析[J]. 大数据, 2020,6(5):2020045-1. DOI： 10.11959/j.issn.2096-0271.2020045.

Ting SONG, Zhanwei CHEN, Haifeng YANG. Aspect sentiment analysis based on a hierarchical attention network[J]. Big Data Research, 2020, 6(5): 2020045-1. DOI： 10.11959/j.issn.2096-0271.2020045.

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。

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%.

关键词

Keywords

references

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