NBWAB: A Model for Text Sentiment Analysis With BERT and ChatGPT
Xiaolei Wang, Hongyun Huang, Zuohua Ding · 2024
Social network texts contain a great deal of sentiment information. Such information reflects the personal attitudes and emotional dispositions for particular topics or events. However, not much comprehensive semantic information and not enough text data are used by traditional text sentiment analysis models, consequently, there are shortcomings in the analysis results. To handle this problem, in this paper, we propose a text sentiment analysis model NBWAB based on BERT-WWM-ATT-BiLSTM text classification. Our optimal model is constructed as follows. BERT-WWM is first used to dynamically encode the character-level and sentence-level features, and then Bi-LSTM is used to capture deeper semantic features of texts. Finally, these results are fused with the relevant multi-dimensional features of texts by multi-head-attention feature fusion skill. To further improve the performance of text sentiment analysis, we employ the ChatGPT data augmentation method to extend training datasets. To show the efficiency of our model, we have conducted experiments on three Chinese datasets: SMP2020-EWECT, Waimai_10k, and Weibo_senti_100k. The accuracy and F1 value of the model on the SMP2020-EWECT dataset (usual) are 80.76% and 77.61%, respectively, the accuracy and F1 value on the Waimai_10k dataset are 92.29% and 91.34%, respectively, and the accuracy and F1 value on the Weibo_senti_100k dataset are 98.10% and 98.24%, respectively. The results show that our model has advantages over the existing models in that more semantic information and more text data are considered for text analysis.