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作者投稿和查稿 主编审稿 专家审稿 编委审稿 远程编辑

计算机工程 ›› 2019, Vol. 45 ›› Issue (3): 286-292. doi: 10.19678/j.issn.1000-3428.0050035

• 开发研究与工程应用 • 上一篇    下一篇

一种用于特定目标情感分析的深度网络模型

基于注意力机制的长短期记忆(LSTM)网络在训练过程中需要耗费大量时间,且仅以句子作为网络输入难以有效区分同一句中不同目标的情感极性。为此,提出一种结合卷积神经网络(CNN)和区域LSTM的深度网络模型。通过区域LSTM实现特定目标的区域划分,在保留特定目标重要情感信息的同时,有效区分不同目标的特征信息,并利用CNN保留整个句子的情感信息。实验结果表明,该模型能有效识别不同目标的情感极性,相比传统网络模型具有更短的模型训练时间。   

  1. 华东师范大学 计算机科学与软件工程学院,上海 200062
  • 收稿日期:2018-01-09 出版日期:2019-03-15 发布日期:2019-03-15
  • 作者简介:陈思远(1993—),男,硕士研究生,主研方向为自然语言处理、情感分析;彭超,副教授;蔡林森、郭兰英,硕士研究生。
  • 基金资助:

    国家自然科学基金(61232006);上海市自然科学基金(14ZR1412400)。

A Deep Network Model for Specific Target Sentiment Analysis

The Long Short Term Memory(LSTM) network based on attention mechanism generally takes a lot of time during the training process,and only uses sentences as a network input,which is difficult to effectively distinguish the different polarities of different targets in the same sentence.To address this problem,this paper proposes a deep neural model of combining Convolutional Neural Network(CNN) and Regional LSTM(CNN-RLSTM).By segmenting the region according to the specific target through the regional LSTM,the feature information of different targets can be effectively distinguished while retaining the specific emotional information of the specific target,and the emotional information of the entire sentence is retained by the CNN.Experimental results show that, the CNN-RLSTM model can effectively identify the emotional polarity of different targets,and the model training time is shorter than the traditional network model.   

  1. School of Computer Science and Software Engineering,East China Normal University,Shanghai 200062,China
  • Received:2018-01-09 Online:2019-03-15 Published:2019-03-15

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摘要：

基于注意力机制的长短期记忆(LSTM)网络在训练过程中需要耗费大量时间,且仅以句子作为网络输入难以有效区分同一句中不同目标的情感极性。为此,提出一种结合卷积神经网络(CNN)和区域LSTM的深度网络模型。通过区域LSTM实现特定目标的区域划分,在保留特定目标重要情感信息的同时,有效区分不同目标的特征信息,并利用CNN保留整个句子的情感信息。实验结果表明,该模型能有效识别不同目标的情感极性,相比传统网络模型具有更短的模型训练时间。

关键词: 深度学习, 情感分析, 特定目标, 卷积神经网络, 长短期记忆网络, 深度网络模型

Abstract:

The Long Short Term Memory(LSTM) network based on attention mechanism generally takes a lot of time during the training process,and only uses sentences as a network input,which is difficult to effectively distinguish the different polarities of different targets in the same sentence.To address this problem,this paper proposes a deep neural model of combining Convolutional Neural Network(CNN) and Regional LSTM(CNN-RLSTM).By segmenting the region according to the specific target through the regional LSTM,the feature information of different targets can be effectively distinguished while retaining the specific emotional information of the specific target,and the emotional information of the entire sentence is retained by the CNN.Experimental results show that, the CNN-RLSTM model can effectively identify the emotional polarity of different targets,and the model training time is shorter than the traditional network model.

Key words: deep learning, sentiment analysis, specific target, Convolutional Neural Network(CNN), Long Short Term Memory(LSTM) network, deep network model

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