Incorporating Hybrid Pooling and Attention Mechanisms for Chinese Text Sentiment Analysis
Xiangkui Jiang, Zhuoxiao Du · 2024
An improved sentiment analysis model is designed to address the problems of increasingly spoken and fragmented web texts and the difficulty of extracting textual sentiment features. At first, a pre-trained model of BERTwwm(Bidirectional Encoder Representations from Transformers-Whole Word Masking) is used to generate textual sentence vector features; second, textual local semantic features are extracted by a hybrid pool of convolutional neural networks and contextual textual semantic features are extracted by a bidirectional recursive gating network that includes an attention mechanism. Finally, the multilevel text features are fused and classified using a classifier. The experimental results show that the F1 values on the microblog text dataset weibo_senti_100k and the COVID-19 pandemic microblog comment dataset are 98.16% and 92.55%,which are better than the benchmark model.