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Chinese Microblog Sentiment Detection Based on CNN-BiGRU and Multihead Attention Mechanism
Scientific Programming ( IF 1.672 ) Pub Date : 2020-10-15 , DOI: 10.1155/2020/8865983
Hong Qiu 1 , Chongdi Fan 2 , Jie Yao 1 , Xiaohan Ye 2
Affiliation  

With the rapid development of the Internet, Weibo has gradually become one of the commonly used social tools in society at present. We can express our opinions on Weibo anytime and anywhere. Weibo is widely used and people can express themselves freely on it; thus, the amount of comments on Weibo has become extremely large. In order to count up the attitudes of users towards a certain event, Weibo managers often need to evaluate the position of a certain microblog in an appropriate way. In traditional position detection tasks, researchers mainly mine text semantic features through constructing feature engineering and sentiment dictionary, but it takes a large amount of manpower in feature selection and design. However, it is an effective method to analyze the sentiment state of microblog comments. Deep learning is developing in an increasingly mature direction, and the utilization of deep learning methods for sentiment detection has become increasingly popular. The application of convolutional neural networks (CNN), bidirectional GRU (BiGRU), and multihead attention mechanism- (multihead attention-) combined method CNN-BiGRU-MAttention (CBMA) to conduct Chinese microblog sentiment detection was proposed in this paper. Firstly, CNN were applied to extract local features of text vectors. Afterward, BiGRU networks were applied to extract the global features of the text to solve the problem that the single CNN cannot obtain global semantic information and the disappearance of the traditional recurrent neural network (RNN) gradient. At last, it was concluded that the CBMA algorithm is more accurate for Chinese microblog sentiment detection through a variety of algorithm experiments.

中文翻译:

基于CNN-BiGRU和多头注意力机制的中文微博情感检测

随着互联网的飞速发展,微博逐渐成为目前社会上常用的社交工具之一。我们可以随时随地在微博上发表意见。微博使用广泛,人们可以在上面自由表达;因此,微博上的评论量变得非常大。为了统计用户对某个事件的态度,微博管理者往往需要以适当的方式评估某个微博的位置。在传统的位置检测任务中,研究人员主要通过构建特征工程和情感词典来挖掘文本语义特征,但在特征选择和设计上需要大量人力。然而,它是分析微博评论情绪状态的有效方法。深度学习正朝着越来越成熟的方向发展,利用深度学习方法进行情感检测也越来越流行。本文提出了应用卷积神经网络(CNN)、双向GRU(BiGRU)和多头注意力机制-(multihead attention-)组合方法CNN-BiGRU-MAttention(CBMA)进行中文微博情感检测。首先,应用CNN提取文本向量的局部特征。之后,应用BiGRU网络提取文本的全局特征,解决了单个CNN无法获取全局语义信息和传统循环神经网络(RNN)梯度消失的问题。最后,
更新日期:2020-10-15
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