基于CNNCIFG-Attention模型的文本情感分类

CNNCIFG-Attention Model for Text Sentiment Classifcation

神经网络在处理中文文本情感分类任务时,文本显著特征提取能力较弱,学习速率也相对缓慢。针对这一问题,文中提出一种基于注意力机制的混合网络模型。首先对文本语料进行预处理,利用传统的卷积神经网络对样本向量的局部信息进行特征提取,并将其输入耦合输入和遗忘门网络模型,用以学习前后词句之间的联系。随后,再加入注意力机制层,对深层次文本信息进行权重分配,提高重要信息对文本情感分类的影响强度。最后,将所提出的混合网络模型在京东商品评论集上进行测试。测试结果显示,新方法的准确率达到了92.13%,F-Score数值为92.06%,证明了CNNCIFG-Attention模型的可行性。

Neural networks are weak in text salient feature extraction and have relatively slow learning rate in processing Chinese text sentiment classification tasks.To solve this problem,this study proposes a hybrid network model based on attention mechanism.This study preprocesses the text corpus,uses the traditional convolutional neural network to extract the feature of the local information of the sample vector.Then,extracted features are input into the coupled input and forget gate network model to learn the connection between the preceding and following words and sentences.Subsequently,the attention mechanism layer is added to assign weights to deep-level text information to improve the intensity of the influence of important information on text sentiment classification.Finally,the proposed hybrid network model is tested on the crawled JD product review collection.The test results show that the accuracy of the proposed method reaches 92.13%,and the F-Score value is 92.06%,which proves the feasibility of the CNNCIFG-Attention model.