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山东大学学报（理学版） ›› 2017, Vol. 52 ›› Issue (9): 19-25.doi: 10.6040/j.issn.1671-9352.1.2016.PC4

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一种基于联合深度学习模型的情感分类方法

杨艳,徐冰*,杨沐昀,赵晶晶   

  1. 哈尔滨工业大学 计算机科学与技术学院 机器智能与翻译实验室, 黑龙江 哈尔滨 150000
  • 收稿日期:2016-11-25 出版日期:2017-09-20 发布日期:2017-09-15
  • 通讯作者: 徐冰(1975— ),女,副教授,博士,研究方向为情感分析.E-mail:hitxb@hit.edu.cn E-mail:yangyan_hit@163.com
  • 作者简介:杨艳(1993— ),女,硕士,研究方向为情感分析.E-mail:yangyan_hit@163.com
  • 基金资助:
    国家自然科学基金资助项目(61402134)

An emotional classification method based on joint deep learning model

YANG Yan, XU Bing*, YANG Mu-yun, ZHAO Jing-jing   

  1. Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150000, Heilongjiang, China
  • Received:2016-11-25 Online:2017-09-20 Published:2017-09-15

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摘要： 针对情感分析问题中长句和短句进行情感分类时不同的建模特点,提出了一种基于联合深度学习模型的情感分类方法。该方法融合长短期记忆模型(LSTM)与卷积神经网络(CNN)对影视评论数据进行情感极性判别,该方法采用LSTM模型对上下文进行建模,通过逐词迭代得到上下文的特征向量,采用CNN模型从词向量序列中自动发现特征,并从局部抽取特征后将局部特征整合成全局特征来提高分类效果。所提出的方法在COAE2016评测的任务2的情感极性分类任务中,其系统准确率获得最好结果。

关键词: LSTM, 情感分类, CNN

Abstract: According to the analysis of emotional problems in the modeling of long and short sentences different characteristics to sentiment classification, this paper proposes a classification algorithm based on the model of joint deep learning. Fusion of long short term memory model of the method(LSTM)and convolutional neural network(CNN)on film reviews emotional polarity discrimination, in the method, LSTM model was used to model context, through the word iteration to get feature vector context, and CNN model was used to automatically discover features from the word vector sequence, and integrating local features from the local feature extraction into global features to improve the classification results. The method proposed in this paper is to obtain the best results of the system accuracy in the task of the COAE2016 evaluation task 2.

Key words: sentiment classification, CNN, LSTM

中图分类号: 

  • TP391.1
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