# Heterogeneous Graph Neural Network for Short Text Classification
## 5 Discussion  
In today's explosive growth of information, the types of text are also divided into more and more fine-grained, in addition to short texts, there are ultra-short texts and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Convergent Epoch** & **Avg. Epoch Time /s** & **Total Time /s** \\ \hline Text GCN & 17 & 1.03 & 17.53 \\ W-W + T-W & 12 & 1.05 & 12.62 \\ W-P + T-W & 11 & 0.58 & 6.48 \\ W-W + W-P & 12 & 1.06 & 12.71 \\ TWPGCN & 12 & 1.09 & 13.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Efficiency comparison experiment. ultra-long texts. This paper focuses on short text classification, mainly because short texts are in the majority on the web, and commentary and news texts are included in the scope of this paper. Subsequent research will be more detailed, and the analysis of comments with emojis or the identification of many fake news on the Internet is a more challenging topic. In the professional field, the classification of paper abstracts is also an interesting application. The paper abstracts contain professional vocabulary and specific phrases, which will require greater word embedding, and will also be a test for the learning and discrimination ability of the model. As the basis of these studies, short text analysis should achieve higher precision, and analyzing short texts from various angles can provide some ideas for the following research. After a long period of study and research, the accuracy of traditional models is not low in the experiments, but it is precisely because has the research gradually matured that it seems to have reached a bottleneck period, and emerging deep learning models are increasingly replacing traditional models. Deep learning enables models to learn and adapt to a wide variety of tasks. Due to different learning methods, the models are good at different fields. CNN models that use adjacent features are good at images, while RNN models that focus on temporal features are more used for text. Thanks to the compositional flexibility of GNN, targeted GNN models can solve more specific tasks, short text classification is one of them. Word embedding technology is an important milestone for the development of NLP, and there are many excellent papers around it. Similar to traditional models, its limitations are gradually exposed as time progresses. This article chooses not to use pre-trained word vectors because of these considerations. Perhaps future research can make more breakthroughs in word embedding, and then combine word embedding with the TWPGCN proposed in this article or other existing models, there will be more better results. In the introduction of this paper, we analyze the research status of short text classification. According to the current research, there is a lack of text classification methods suitable for short texts. Traditional text classification methods are not targeted enough, and methods based on pre-training and external knowledge bases are not necessarily suitable for all occasions. Generally speaking, methods based on GNN are more suitable for short text classification, the TWPGCN proposed in this paper is based on GNN. First of all, this paper composes graph for short texts, and extracts text, words, and POS tags in short texts as nodes to construct three kinds of graphs, namely W-W graph, W-P graph, and T-W graph. Then, based on these small graphs, a heterogeneous graph suitable for short text classification is constructed, and a TWPGCN suitable for the above heterogeneous graph is designed to learn it. After that, this paper conducts a comparative experiment on the existing short text classification methods, and analyzes the advantages and disadvantages of various models. In this paper, an ablation experiment is carried out for the proposed method, and the data is experimentally compared on the W-W graph, W-P graph and T-W graph, and the W-W + T-W graph, W-W + W-P after combining the three small graphs in pairs.