Sentiment classification method based on BERT-CondConv multi-moment state fusion

Abstract

Sentiment classification has emerged as a significant research area in the field of natural language processing, garnering considerable attention in recent years. However, obtaining feature information of text sequences for sentiment classification, especially for texts with diverse characteristics, remains a challenging task. Traditional methods for extracting text features often treat all data in a uniform manner. To address this issue, we propose a hybrid sentiment classification model called BERT-CondConv, which integrates the strengths of BERT and conditional parameter convolution networks. By applying adaptive conditional parameter convolution on the hidden state feature information at different time steps of BERT, our model enhances feature extraction and optimization, and finally fusion features, thus improving the sentiment classification task. We compared various base model architectures and benchmarked our method against state-of-the-art techniques. The experimental results demonstrate the effectiveness of our approach.