基于多特征和深度神经网络的维吾尔文情感分类

Uyghur sentiment classification based on multi-features and deep neural network

针对传统机器学习的情感分类方法存在长距离依赖问题与深度学习存在忽略情感词库的弊端,提出了一种基于注意力机制与双向长短记忆网络和卷积神经网络模型相结合的维吾尔文情感分类方法。将多特征拼接向量作为双向长短记忆网络的输入来捕获文本上下文信息,使用注意力机制和卷积网络获取文本隐藏情感特征信息,有效增强了对文本情感语义的捕获能力。实验结果表明,该方法在二分类和五分类情感数据集上的F1值相比于机器学习方法分别提高了5.59%和7.73%。

In order to solve the problem of long-distance dependence in traditional machine learning sentiment classification method and the disadvantage of ignoring the emotional lexicon in deep learning,this paper proposed a Uyghur sentiment classification method based on attention mechanism combined with bidirectional long-short term memory network and convolutional neural network model.It used the concatenated multi-feature vector as the input of the bidirectional long short-term memory network to capture the context information,and used the attention mechanism and convolution network to capture text hidden emotional feature information,which effectively enhanced the capture ability of the text sentiment semantics.The experimental results show that the F1 value of this method on two-category and five-category Uyghur sentiment data sets are higher than machine learning method 5.59%and 7.73%respectively.