# A Sequential Graph Neural Network for Short Text Classification
## 4 Results and Discussion
### Test Performance  
Tables 3 and 4 show the test accuracy and test macro-f1 of all baselines and our two models for all datasets, respectively. We can see that our model achieves optimal results on all datasets, especially for datasets with short average text lengths, such as Ag news sub, MR, Searchsnippets, SMS, and Biomedical. SSGNN has performed better than other baselines, which proves the effectiveness of our method on short text datasets. In addition, ESGNN performs better than SGNN, which shows that the feature extraction ability of the model is more powerful for short text classification. Moreover, we also evaluate the model efficiency by the training time of per epoch, as shown in Table 5; it can be seen that in addition to TextGCN, our method shows better advantages compared with other GNN-based methods, which may be because TextGCN builds a large corpus graph and only captures the structural information of each document. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **R8** & **R52** & **Agnewssub** & **MR** & **Searchsnippets** & **SMS** & **Biomedical** \\ \hline TextCNN & 93.24 & 60.93 & 88.14 & 77.25 & 88.12 & 97.21 & 71.95 \\ Bi-LSTM & 93.68 & 63.10 & 87.06 & 77.13 & 83.62 & 96.91 & 60.08 \\ fastText & 90.76 & 57.98 & 88.10 & 74.45 & 87.33 & 95.67 & 60.55 \\ SWEM & 89.29 & 48.27 & 86.98 & 75.67 & 87.06 & 95.42 & 68.19 \\ TextGCN & 93.38 & 67.79 & 86.88 & 76.24 & 82.74 & 95.62 & 69.76 \\ \hline HeteGCN & 92.33 & 66.53 & - & 75.62 & - & - & - \\ S-LSTM & 93.80 & 73.33 & 87.44 & 76.96 & 86.63 & 96.03 & 71.53 \\ TextING & 93.62 & 73.38 & 88.89 & 79.02 & 86.26 & 97.19 & 72.14 \\ \hline ESGNN & **94.31** & **74.54** & **88.95** & **79.83** & **89.10** & **97.87** & **73.02** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Test Macro-F1 (%) for various models on different datasets. We ran all models 10 times and reported the average as the experimental result. Best results are shown in bold. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **R8** & **R52** & **Agnewssub** & **MR** & **Searchsnippets** & **SMS** & **Biomedical** \\ \hline TextCNN & 95.71 \(\pm\) 0.52 & 87.59 \(\pm\) 0.48 & 88.74 \(\pm\) 0.16 & 77.75 \(\pm\) 0.72 & 89.52 \(\pm\) 0.35 & 99.03 \(\pm\) 0.05 & 73.08 \(\pm\) 0.33 \\ Bi-LSTM & 96.31 \(\pm\) 0.33 & 90.54 \(\pm\) 0.91 & 87.68 \(\pm\) 0.35 & 77.68 \(\pm\) 0.86 & 84.81 \(\pm\) 1.40 & 98.77 \(\pm\) 0.10 & 63.42 \(\pm\) 0.97 \\ fastText & 96.13 \(\pm\) 0.21 & 92.81 \(\pm\) 0.09 & 88.22 \(\pm\) 0.04 & 75.14 \(\pm\) 0.20 & 88.56 \(\pm\) 0.12 & 98.84 \(\pm\) 0.06 & 65.17 \(\pm\) 0.22 \\ SWEM & 95.32 \(\pm\) 0.26 & 92.94 \(\pm\) 0.24 & 87.77 \(\pm\) 0.05 & 76.65 \(\pm\) 0.63 & 87.36 \(\pm\) 0.32 & 98.33 \(\pm\) 0.11 & 68.97 \(\pm\) 0.20 \\ \hline TextGCN & 97.07 \(\pm\) 0.10 & 93.56 \(\pm\) 0.18 & 87.55 \(\pm\) 0.10 & 76.74 \(\pm\) 0.20 & 83.49 \(\pm\) 0.20 & 98.30 \(\pm\) 0.05 & 69.67 \(\pm\) 0.20 \\ TensorGCN & 98.04 \(\pm\) 0.08 & 95.05 \(\pm\) 0.11 & - & 77.91 \(\pm\) 0.07 & - & - & - \\ S-LSTM & 97.67 \(\pm\) 0.14 & 94.92 \(\pm\) 0.19 & 88.21 \(\pm\) 0.38 & 77.75 \(\pm\) 0.31 & 87.54 \(\pm\) 0.33 & 98.60 \(\pm\) 0.08 & 73.77 \(\pm\) 0.20 \\ TextING & 98.04 \(\pm\) 0.25 & 95.48 \(\pm\) 0.19 & 89.24 \(\pm\) 0.20 & 79.82 \(\pm\) 0.20 & 87.03 \(\pm\) 0.43 & 98.89 \(\pm\) 0.19 & 73.88 \(\pm\) 0.50 \\ \hline SGNN & 98.09 \(\pm\) 0.08 & 95.46 \(\pm\) 0.15 & 89.57 \(\pm\) 0.25 & 80.58 \(\pm\) 0.18 & 90.68 \(\pm\) 0.32 & 99.22 \(\pm\) 0.11 & 74.92 \(\pm\) 0.34 \\ ESGNN & **98.23 \(\pm\) 0.09** & **95.72 \(\pm\) 0.16** & **89.66 \(\pm\) 0.18** & **80.93 \(\pm\) 0.14** & **90.80 \(\pm\) 0.21** & **99.31 \(\pm\) 0.06** & **75.34 \(\pm\) 0.36** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test accuracy (%) for various models on different datasets. We ran all models 10 times and reported the average \(\pm\) standard deviation as the experimental result. Best results are shown in bold.