The difference between PGCN and TextGCN is that PGCN includes sequence position information while TextGCN does not. In the 20NG dataset, PGCN is increased by 1.11% (an improvement of 0.74% on the R8 dataset; 1.46% on the R52 dataset; Ohsumed and MR increased by 3.72% and 10.88%). At the same time, position information is added into GAT for experiment (i.e . the PBGAT model in Table 3). As can be seen from Table 3, the classification accuracy of PBGAT is higher than that of PGCN on the five datasets; adding position information into the network can significantly improve the classification accuracy, especially in the sentiment classification dataset MR. According to the analysis presented herein, because the task of emotion classification is closely related to word order, the effect on MR is significantly improved: for example, “I like this actor but I don’t like this movie” without the position information, the model cannot tell where the actor is in relation to the movie. Once the positions are reversed, the emotion of the whole sentence is reversed. Therefore, the improvement of sentiment analysis dataset is the most obvious, which shows the necessity of adding position information.
To verify the effectiveness of edge features in improving network performance, a PBGCN and a PEGCN are compared here. The PEGCN model processes the adjacency matrix based on PBGCN and makes full use of the multi-dimensional features of the edge. The only difference between the two is the processing of the edge features. As seen from Table 3, the classification accuracy of PEGCN on five benchmark datasets has been improved, and the optimal classification effect has been achieved on all datasets. In particular, for the 20NG and MR datasets, the accuracy is improved by 2.62% and 1.89%, respectively. The analysis of this study: Compared with the edge features represented by the adjacency matrix in the past, the tensor after dimension enhancement has richer semantics. Discretized points can only indicate whether there is a connection between nodes, that is, connectivity features; while the edge matrix after dimension enhancement has P additional dimensional features, which can be used to represent more information between nodes, such as connection types, connection strength, etc., making full use of edge features helps the network to train better node representations, thereby improving the classification task. It can be seen from Table 3 that the full extraction of edge features has a certain effect on GCN to improve the accuracy of text classification.
To make a comparison with similar models, BERTGCN26 is used as the baseline model: BERTGCN is the model combining BERT and GCN. The model proposed herein is compared with the model also combined with BERT and GCN (Table 4).
Table 4. Comparison of Classification Accuracy of Similar Models. metric: accuracy (%)