融合非负正弦位置编码和混合注意力机制的情感分析模型OA北大核心CSTPCD

NSPEHA-BiLSTM is proposed to address the issues of sequence models in sentiment analysis tasks,such as difficulty in obtaining the relative positional information of text and the loss of critical information when processing long sequences.The model integrates non-negative sinusoidal position encoding(NSPE)and hybrid attention mechanism(HAM)to incorporate relative positional information into word embeddings and weight the global and local information features of text using HAM,respectively,ensuring the accurate transmission of critical information.The text features are extracted by Bi-LSTM,and sentiment analysis is performed using a fully connected layer.NSPEHA-BiLSTM achieves higher accu-racy than Bi-LSTM and Text-CNN by 4.67 and 2.02 percentage points,respectively,on the IMDB dataset,and the model performance improves with longer input text.The results also verify that NSPE is superior to other position encodings.