混合神经网络和条件随机场相结合的文本情感分析

针对当前文本情感分析中神经网络模型训练时间长,上下文信息学习不足的问题,该文提出了一种结合混合神经网络和条件随机场(conditional random fields,CRF)的模型.该模型将神经网络作为语言模型,结合了卷积神经网络(convolutional neural networks,CNN)与双向门控循环单元...

Text sentiment analysis combining hybrid neural network and conditional random field

To solve problems such as the long training time of neural network models and insufficient contextual-information learning in text sentiment analysis, in this paper, we propose a model that combines a hybrid neural network with the conditional random field (CRF). Taking the neural network as the language model, the model combines the semantic information and structural features of the convolutional neural network with the bi-directional gated recurrent unit. The CRF model is used as a classifier that determines the probability distributions of emotions, from which it can then accurately determine the emotion category. The model was tested on the NLPCC 2014 data set, and achieved an accuracy rate of 91.74%. Compared with other classification models, the proposed model can obtain better accuracy and F values.