Research on Social Comment Text Classification Based on BERT and Graph Neural Network

Abstract

In the era of information data explosion, accurately classifying massive text data and mining emotional tendencies play an important role in purifying the network environment. A sentiment classification model that combines the advantages of BERT and graph neural networks is proposed to address the shortcomings of a single traditional classification model in terms of classification accuracy and efficiency. It is recommended to combine the transformer based BERT model with graph neural networks, using the text embedding technology in BERT to leverage its powerful feature extraction ability and the advantage of graph neural networks in aggregating semantic features of text structure information. This will advance the semantic information of the entire text and then pass the semantic features to the combined model for classification. At the same time, in text sequence processing, it is proposed to construct an emotional dependency tree and obtain a leading matrix based on dependency relationships, which can improve the model's understanding of text semantic and structural information. The model was tested on English datasets such as 20NG and R8, as well as the Weibo 100k dataset, with a particularly high rate of 93.7% on the Weibo 100k dataset. Comparative experiments were conducted with other models.The results indicate that compared with other segmentation models, our model has better segmentation performance and can effectively aggregate semantic information of comment texts.