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.

[2020-05-05] Printed Article /doc/2018.10.0809/v2