Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (7): 167-175.DOI: 10.3778/j.issn.1002-8331.2010-0029

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Implicit Sentiment Analysis Based on Context Aware Tree Recurrent Neutral Network

CHEN Qiuchang, ZHAO Hui, ZUO Enguang, ZHAO Yuxia, WEI Wenyu   

  1. 1.College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
    2.College of Mathematics and Computer Application, Shangluo University, Shangluo, Shaanxi 726000, China
  • Online:2022-04-01 Published:2022-04-01



  1. 1.新疆大学 信息科学与工程学院,乌鲁木齐 830046 
    2.商洛学院 数学与计算机应用学院,陕西 商洛 726000

Abstract: Aiming at the problems of inaccurate feature extraction and gradient explodes or gradient disappears of text information in Chinese implicit sentiment analysis by existing serialization models, a parallel hybrid model of bidirectional long short time neural network and context-aware tree recurrent neural network(CA-TRNN) is proposed. Firstly, it is used to extract the context information from the text by BiLSTM, and is extracted the semantic feature information of the target sentence in the text by the tree recurrent neural network(TRNN). Finally, two representations are fused by the attention mechanism of the specific target sentence, and then the sentiment classification results of the text are obtained by using softmax. The experimental results in the Chinese implicit sentiment analysis task of SMP2019 micro blog show that the model CA-TRNN can effectively improve the accuracy of classification results with low time cost and better application ability.

Key words: context-aware, attention mechanism, tree recurrent neutral network(TRNN), implicit sentiment analysis

摘要: 针对现有的序列化模型对中文隐式情感分析中特征信息提取不准确以及对篇章级的文本信息提取存在的梯度爆炸或者梯度消失的问题,提出了双向长短时神经网络和上下文感知的树形递归神经网络(context-aware tree recurrent neutral network,CA-TRNN)的并行混合模型。该模型分别利用双向循环长短时记忆神经网络(BiLSTM)提取文本中的上下文信息,树形递归神经网络(TRNN)提取文本中目标句的语义特征信息,最后,使用特定目标句的注意力机制将两个表示信息进行融合表示后,经过softmax得出文本的情感分类结果。采用SMP2019微博中文隐式情感分析任务中的数据进行验证,实验结果表明,所使用的模型(CA-TRNN)可以有效提高分类结果的准确度,时间代价小,具有更好的应用能力。

关键词: 上下文感知, 注意力机制, 树形递归神经网络(TRNN), 隐式情感分析