【摘要】 针对Word2vec等静态词向量模型不能解决一词多义、传统情感分析模型不能同时提取文本的全局和局部信息问题，本文提出了结合广义自回归预训练语言模型(XLNet)的文本情感分析模型。首先利用XLNet表示文本特征，然后利用卷积神经网络提取文本向量的局部特征，再利用双向门控循环单元提取文本的更深层次上下文信息，最后引入注意力机制，根据特征的重要性赋予特征不同的权重，并进行文本情感极性分析。仿真实验中将本文模型与5种常用的情感分析模型进行对比，验证了模型的准确率和优越性。更多还原

【Abstract】 To address the problems that static word vector models such as Word2 vec cannot address multiple meanings of words and traditional sentiment analysis models cannot extract both global and local information of texts, a text sentiment analysis model, which combines generalized autoregressive pre-trained language model(XLNet),is proposed in this paper. Firstly, the text features are represented by XLNet. Secondly, local features of the text vector are extracted by convolutional neural network. Thirdly, the deeper contextual information of the text is extracted by a bi-directional gated recurrent unit. Finally, an attention mechanism is introduced to assign different weights to the features according to their importance, and text sentiment polarity analysis is performed. Simulation experiments are conducted to compare this model with five commonly used sentiment analysis models to verify the accuracy and superiority of the model.更多还原