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Network text sentiment analysis method combining LDA text representation and GRU-CNN

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Abstract

In order to improve the performance of internet public sentiment analysis, a text sentiment analysis method combining Latent Dirichlet Allocation (LDA) text representation and convolutional neural network (CNN) is proposed. First, the review texts are collected from the network for preprocessing. Then, using the LDA topic model to train the latent semantic space representation (topic distribution) of the short text, and the short text feature vector representation based on the topic distribution is constructed. Finally, the CNN with gated recurrent unit (GRU) is used as a classifier. According to the input feature matrix, the GRU-CNN strengthens the relationship between words and words, text and text, so as to achieve high accurate text classification. The simulation results show that this method can effectively improve the accuracy of text sentiment classification.

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Authors and Affiliations

  1. Zhejiang Agricultural Business College, Hangzhou, Zhejiang, China

    Li-xia Luo

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  1. Li-xia Luo
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Correspondence to Li-xia Luo.

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Luo, Lx. Network text sentiment analysis method combining LDA text representation and GRU-CNN. Pers Ubiquit Comput 23, 405–412 (2019). https://doi.org/10.1007/s00779-018-1183-9

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  • DOI: https://doi.org/10.1007/s00779-018-1183-9

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