Text Sentiment Analysis of Douban Film Short Comments Based on BERT-CNN-BiLSTM-Att Model

To solve the problems of polysemy and feature extraction in the text sentiment analysis process, a BERT-CNN-BiLSTM-Att hybrid model has been proposed for text sentiment analysis. The BERT pre-training model was established to break up the text input into words and obtain a dynamic word vector that was then input into the CNN and the BiLSTM models respectively. Later, the local features of the word vector, extracted using CNN, and the global features, extracted using BiLSTM, were fused, and the key information of the Douban movie review dataset was highlighted using the attention mechanism to realize sentiment categorization of the dataset. The results of comparison between the constructed model and Word2Vec-BiLSTM, Word2Vec-CNN, Word2Vec-CNN-BiLSTM-Att, BERT, BERT-CNN and BERT-BiLSTM models show that the model that runs against the test dataset has an increased accuracy by 4.63%,4.37%,3.64%,2.63%,2.56% and 5.54% respectively. The experimental findings reveal that BERT-CNN-BiLSTM-Att’s sentiment analysis method is more accurate in performing sentiment classification.