Sentiment Analysis of the Consumer Review Text Based on BERT-BiLSTM in a Social Media Environment

Abstract

In this paper, a BERT-BiLSTM-based consumer review text sentiment analysis method in the e-commerce big data field is proposed. First, the unlabeled text is trained using the BERT training model for the language introduced in the deep learning, and then the pre-training model of the text data is delivered by the learning textual features and data to extract deeper vectors. Second, the BiLSTM model is applied to simultaneously obtain contextual information so as to illustrate optimal textual features. Finally, a corresponding sentiment analysis model relative to the consumer review text is constructed by combining the BERT model with BiLSTM to better merge the context for classifying sentiment and improving the final feature vector accuracy for the sentiment classification results. Simulated by experiments, the method proposed in this paper was compared with another three methods using the same data set. The results obtained indicate that the proposed method has the highest precision, recall, and F1-Measure, and the values reach 92.64%, 90.32%, and 91.46%, respectively.