# Heterogeneous Graph-Convolution-Network-Based Short-Text Classification
## 4 Experiment and Performance Analysis
### Experimental Results and Analysis
In addition, the word co-occurrence feature between words is used as the weight of edges for global sharing, which has more advantages than the local information sharing of traditional models. On the MR dataset, the accuracy of the TextGCN model is lower than that of the CNN and BiLSTM models. The main reason is that TextGCN ignores the role of word order in emotion classification, while CNN and BiLSTM construct continuous word sequences. In addition, compared with other datasets, the text of the MR dataset is too short, resulting in a smaller text graph being formed, which restricts the transmission of information between nodes. SHGCN has the highest average accuracy on three datasets, and its accuracy on both the AGNews and MR datasets is also higher than that of other models. The accuracy of SHGCN on the R52 dataset is slightly lower than that of the TensorGCN model. The text length of the R52 dataset is relatively long, and since the TensorGCN model incorporates syntactic features, it is more suitable for datasets with long text lengths. The average accuracy of SHGCN is 5.5% and 1.8% higher than that of the TextGCN and the TensorGCN models. SHGCN integrates the entity information corresponding to a word in the heterogeneous graph and transmits the entity information to the adjacent document nodes and word nodes through the graph convolution neural network, which enriches the semantic expression of the document and word nodes. At the same time, the introduction of entity information alleviates the problem of word ambiguity, so it helps to obtain more accurate expressions. In addition, SHGCN represents the features of words and document nodes through BiLSTM, which can better capture the context semantic information of documents and has achieved good results in classification tasks that rely on word orders. Due to the utilization of the correlation between nodes and the use of a bidirectional long short-term memory network to further explore the semantic features of text context, SHGCN achieves good results in experiments, as shown in Tables 4 and 5. In experiments on three typical datasets, compared with traditional machine learning models and mainstream deep learning models, our model achieves the best accuracy, the best precision, and the best recall rate, which verifies the practicality of our model. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**AGNews**} & \multicolumn{2}{c}{**R52**} & \multicolumn{2}{c}{**MR**} & \multirow{2}{*}{**Mean Value**} \\  & \multicolumn{2}{c}{**Standard**} & & & **Standard** & & **Standard** \\  & \multicolumn{2}{c}{**Deviation**} & & & **Deviation** & & **Deviation** & \\ \hline TF-IDF + LR & 87.50 & 0.42 & 88.77 & 0.76 & 74.34 & 0.91 & 83.54 \\ CNN & 85.88 & 0.28 & 90.81 & 0.17 & 73.84 & 0.30 & 83.51 \\ BiLSTM & 81.91 & 1.42 & 86.12 & 0.68 & 71.76 & 1.41 & 79.93 \\ fastText & 76.62 & 1.74 & 90.34 & 0.17 & 69.09 & 1.84 & 78.69 \\ TextGCN & 79.26 & 7.81 & 92.81 & 0.61 & 76.54 & 0.16 & 82.87 \\ TensorGCN & 87.05 & 0.64 & 94.81 & 0.29 & 77.84 & 0.41 & 86.57 \\ SHGCN & 88.38 & 0.93 & 93.87 & 0.30 & 82.87 & 0.11 & 88.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of accuracy of different models.