Text Sentiment Analysis Based on a New Hybrid Network Model

Abstract

The research of text sentiment analysis based on deep learning is increasingly rich, but the current models still have different degrees of deviation in understanding of semantic information. In order to reduce the loss of semantic information and improve the prediction accuracy as much as possible, the paper creatively combines the doc2vec model with the deep learning model and attention mechanism and proposes a new hybrid sentiment analysis model based on the doc2vec + CNN + BiLSTM + Attention. The new hybrid model effectively exploits the structural features of each part. In the model, the understanding of the overall semantic information of the sentence is enhanced through the paragraph vector pretrained by the doc2vec structure which can effectively reduce the loss of semantic information. The local features of the text are extracted through the CNN structure. The context information interaction is completed through the bidirectional cycle structure of the BiLSTM. The performance is improved by allocating weight and resources to the text information of different importance through the attention mechanism. The new model was built based on Keras framework, and performance comparison experiments and analysis were performed on the IMDB dataset and the DailyDialog dataset. The results have shown that the accuracy of the new model on the two datasets is 91.3% and 93.3%, respectively, and the loss rate is 22.1% and 19.9%, respectively. The accuracy on the IMDB datasets is 1.0% and 0.5% higher than that of the CNN-BiLSTM-Attention model and ATT-MCNN-BGRUM model in the references. Comprehensive comparison has shown the overall performance is improved, and the new model is effective.