Research On Text Sentiment Classification Based On Attention-CNN

Text sentiment classification is the use of computer technology to process data containing emotional features,which can identify and extract subjective information in text.Because the text sentiment classification based on traditional methods is not sufficient for feature information extraction,and the text sentiment classification method based on deep learning can improve this problem,deep learning has become the main research method of text sentiment classification.In order to extract more feature information and improve classification accuracy,the paper proposes a non-static FastText word vector model and Attention-CNN fusion model.The main research contents of the thesis are as follows:(1)A method based on non-static FastText word vectors is proposed.This method uses the FastText model to train the word vector to obtain the initial word vector matrix,which is used as parameter training during the training process.Through continuous adjustment and update,the word vector is more suitable for the text emotion classification task,and more words are obtained.The semantic and grammatical relationship of the,so as to improve the classification accuracy.Perform text sentiment classification experiments on convolutional neural networks.Compared with the static FastText,Word2Vec and Doc2vec models,the non-static FastText model has increased accuracy by 2.54%,6.22%and 4.82%on the public comment data set;On the hotel review corpus,the accuracy rate increased by 1.84%,6.22%and 5.1 0%.(2)A fusion model based on Attention-CNN is proposed.The model combines convolutional neural network,gated neural network and attention mechanism.Aiming at the inability of convolutional neural network to capture long-distance dependencies,the extracted features are not sufficient For the problem,firstly determine the value of related hyperparameters through experiments,including Dropout value,batch-size parameter and activation function,etc;then conduct model comparison experiments,compared with CNN,LSTM and GRU,based on Attention-CNN fusion On the public review data set,the accuracy of the model increased by 4.70%,3.66%,and 2.95%,and on the hotel review corpus,the accuracy increased by 3.61%,2.88%,and 2.03%.(3)Constructed a text content sentiment classification system.The system verifies the effectiveness of the non-static FastText word vector and the Attention-CNN-based fusion model.The system is implemented in Python,through visual operation,the specific details of the classification results can be clearly displayed,which has certain use value in life.