Abstract: Sentiment analysis of massive text information on social networks can better mine the behavior rules of Internet users,helping decision-making institutions understand the public opinion tendencies and helping businesses improve the quality of service. The task of Chinese implicit sentiment classification is more difficult than those of other languages due to the absence of key emotional features,expression vector forms and cultural customs. The existing Chinese implicit sentiment classification methods are mainly based on Convolutional Neural Network(CNN),and have some defects, such as the inability to obtain the sequence of words and not using contextual emotional features reasonably in implicit emotion discrimination. A Chinese implicit sentiment classification model combining sequence and contextual features named GGBA (GCNN-GRU-BiGRU-Attention) was proposed to solve the above problems. In the model, Gated Convolutional Neural Network (GCNN) was used to extract the local important information of sentences with implicit sentiments,and Gated Recurrent Unit(GRU)network was used to enhance the temporal information of features. In the context feature processing of sentences with implicit sentiments,the combination of Bidirectional Gated Recurrent Unit (BiGRU)and attention was used to extract the important emotional features. After obtaining the two types of features,the contextual important features were integrated into the implicit emotion discrimination through the fusion layer. Experimental results on the implicit sentiment analysis evaluation dataset showed that the macro average precision of GGBA model was 3. 72% higher than that of normal text CNN named TextCNN,2. 57% higher than that of GRU,and 1. 90% higher than that of Disconnected Recurrent Neural Network(DRNN). Therefore,GGBA model achieves better classification performance than the basic models in implicit sentiment analysis tasks.

Key words: Chinese implicit sentiment classification, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), contextual feature, attention mechanism

摘要： 对社交网络上的海量文本信息进行情感分析可以更好地挖掘网民行为规律，从而帮助决策机构了解舆情倾向以及帮助商家改善服务质量。由于不存在关键情感特征、表达载体形式和文化习俗等因素的影响，中文隐式情感分类任务比其他语言更加困难。已有的中文隐式情感分类方法以卷积神经网络（CNN）为主，这些方法存在着无法获取词语的时序信息和在隐式情感判别中未合理利用上下文情感特征的缺陷。为了解决以上问题，采用门控卷积神经网络（GCNN）提取隐式情感句的局部重要信息，采用门控循环单元（GRU）网络增强特征的时序信息；而在隐式情感句的上下文特征处理上，采用双向门控循环单元（BiGRU）+注意力机制（Attention）的组合提取重要情感特征；在获得两种特征后，通过融合层将上下文重要特征融入到隐式情感判别中；最后得到的融合时序和上下文特征的中文隐式情感分类模型被命名为GGBA。在隐式情感分析评测数据集上进行实验，结果表明所提出的GGBA模型在宏平均准确率上比普通的文本CNN即TextCNN提高了3.72%、比GRU提高了2.57%、比中断循环神经网络（DRNN）提高了1.90%，由此可见， GGBA模型在隐式情感分析任务中比基础模型获得了更好的分类性能。

关键词: 中文隐式情感分类, 卷积神经网络, 循环神经网络, 上下文特征, 注意力机制