基于卷积神经网络和双向门控循环单元网络注意力机制的情感分析

传统的情感分析方法不能获取全局特征,以及否定词、转折词和程度副词的出现影响句子极性判断.在深度学习方法基础上提出了基于卷积神经网络和双向门控循环单元网络注意力机制的短文本情感分析方法.将情感积分引入卷积神经网络,利用情感词自身信息,通过双向门控循环网络模型获取全局特征,对影响句子极性的否定词、转折词和程度副词引入注意力机制实现对这类词的重点关注,提取影响句子极性的重要信息.实验结果表明,该模型与现有相关模型相比,有效提高情感分类的准确率.

Sentiment Analysis of Short Text Based on convolutional neural network and bidirectional gated recurrent unit Attention

Sentiment analysis has been one of the research hotspots of natural language processing. The traditional sentiment analysis method can not obtain the global features well,and negative words, turning words and degree adverbs affect the sentence polarity judgment. According to the deep learning method, this paper proposes a short text sentiment analysis based on the CNN (Convolutional Neural Networks) and the attention mechanism of the Bi-GRU (Bidirectional Gated Recurrent Unit). In view of the CNN, the emotional integration is introduced to make full use of the emotional word self-information, and the global feature is obtained through the Bi-GRU.Further more the negative words, turning words and degree adverbs, which affect the polarity of the sentence, are introduced into the attention mechanism to retain important information. The experimental results show that the model can effectively improve the accuracy of sentiment classification compared with the existing related models.