Nowadays, more and more users are playing an important role on the Internet. The more comments they post, the more information they contain and the more informative they are. In order to analyze the sentiment orientation of users' comments more accurately, the text Vit-BiGRU-Attention sentiment classification model is based on BiGRU and Attention mechanisms. First, the CBOW model is used to train the word vector. Second, BiGRU is combined with Viterbi algorithm to extract contextual features of the text by combining forward and backward hidden layers. Then, different weights are assigned to words by the attention mechanism to enhance the understanding of emotions and determine the polarity of emotions. Finally, the output is passed through a softmax classifier. The experimental results show that the accuracy of the model has been greatly improved.
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