Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (8): 2192-2197.DOI: 10.11772/j.issn.1001-9081.2018122552

• Artificial intelligence • Previous Articles     Next Articles

Short text sentiment analysis based on parallel hybrid neural network model

CHEN Jie, SHAO Zhiqing, ZHANG Huanhuan, FEI Jiahui   

  1. School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
  • Received:2018-12-26 Revised:2019-02-24 Online:2019-08-10 Published:2019-03-29
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61462073).


陈洁, 邵志清, 张欢欢, 费佳慧   

  1. 华东理工大学 信息科学与工程学院, 上海 200237
  • 通讯作者: 邵志清
  • 作者简介:陈洁(1994-),男,江苏南通人,硕士研究生,主要研究方向:自然语言处理、人工智能;邵志清(1966-),男,江苏常熟人,教授,博士生导师,博士,主要研究方向:网络计算及应用、大数据、软件方法学;张欢欢(1968-),女,黑龙江哈尔滨人,副教授,博士,主要研究方向:智能信息服务、形式化方法;费佳慧(1992-),男,江苏南通人,硕士研究生,主要研究方向:机器学习、自然语言处理。
  • 基金资助:

Abstract: Concerning the problems that the traditional Convolutional Neural Network (CNN) ignores the contextual semantics of words when performing sentiment analysis tasks and CNN loses a lot of feature information during max pooling operation at the pooling layer, which limit the text classification performance of model, a parallel hybrid neural network model, namely CA-BGA (Convolutional Neural Network Attention and Bidirectional Gated Recurrent Unit Attention), was proposed. Firstly, a feature fusion method was adopted to integrate Bidirectional Gated Recurrent Unit (BiGRU) into the output of CNN, thus semantic learning was enhanced by integrating the global semantic features of sentences. Then, the attention mechanism was introduced between the convolutional layer and the pooling layer of CNN and at the output of BiGRU to reduce noise interference while retaining more feature information. Finally, a parallel hybrid neural network model was constructed based on the above two improvement strategies. Experimental results show that the proposed hybrid neural network model has the characteristic of fast convergence, and effectively improves the F1 value of text classification. The proposed model has excellent performance in Chinese short text sentiment analysis tasks.

Key words: Convolutional Neural Network (CNN), feature fusion, Bidirectional Gated Recurrent Unit (BiGRU), attention mechanism, short text sentiment analysis

摘要: 针对传统的卷积神经网络(CNN)在进行情感分析任务时会忽略词的上下文语义以及CNN在最大池化操作时会丢失大量特征信息,从而限制模型的文本分类性能这两大问题,提出一种并行混合神经网络模型CA-BGA。首先,采用特征融合的方法在CNN的输出端融入双向门限循环单元(BiGRU)神经网络,通过融合句子的全局语义特征加强语义学习;然后,在CNN的卷积层和池化层之间以及BiGRU的输出端引入注意力机制,从而在保留较多特征信息的同时,降低噪声干扰;最后,基于以上两种改进策略构造出了并行混合神经网络模型。实验结果表明,提出的混合神经网络模型具有收敛速度快的特性,并且有效地提升了文本分类的F1值,在中文评论短文本情感分析任务上具有优良的性能。

关键词: 卷积神经网络, 特征融合, 双向门限循环单元, 注意力机制, 短文本情感分析

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