A BWCNN-GRU-LSTM-ATT model-based sentiment classification method for civil aviation passengers

Abstract

To address the problems that a single Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network cannot determine the importance of each word in the text when extracting features from the text and the high time complexity of the network, this paper proposes a text classification method based on a multi-channel This paper proposes a text classification model (BWCNN-GRU-LSTM-ATT) based on multi-channel CNN, Gated Recurrent Unit Network (GRU), LSTM and Attention mechanism (ATT) to solve the problem of inadequacy of a single method, so as to achieve accurate classification of civil aviation passengers' emotions. Firstly, we use word embedding to vectorize the input text, extract the local features of the text through multi-channel CNN, and then integrate the full text semantics; make full use of the advantages of GRU and LSTM, and design a GRU-LSTM tandem network model to increase the effective extraction of contextual semantic information; then increase the weight of keywords by adding the attention mechanism to fully extract the features; finally, we adopt the Bayesian optimization algorithm is adopted to optimize the model parameters and construct the optimized sentiment classification of civil aviation passengers. The results show that the classification accuracy reaches 99.2% and achieves better classification results.