# Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks
## 4 Experiment
### Experimental and Result Analysis  
#### 4.3.1 Comparative Experiments on News Datasets  
To assess the effectiveness of the proposed model, we conducted comparative experiments against seven representative baseline models: TextCNN [8], BiLSTM-Attention [45], GCNN [39], DPCNN [26], FastText [46], CNN-BiLSTM [47], and CNN-BiGRU-Attention [48]. All models were trained under identical conditions, including the training parameters and data preprocessing strategies, to ensure a fair comparison. For consistency, the hidden size of the BiGRU layers was set to 128, the same as that used in BiLSTM. Accuracy and F1-score were adopted as evaluation metrics to comprehensively measure classification performance. Table 5 reports the classification outcomes of our model versus baseline approaches on the two benchmark datasets. Table 5 demonstrates that the model proposed in this paper achieved the highest accuracy and F1-score on both datasets, surpassing all baseline methods. Specifically, \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Word2 Vec embedding dimension & 300 \\ POS embedding dimension & 10 \\ convolution kernel widths & \{2,3\} \\ number of convolution kernels & 128 \\ BiLSTM hidden units & 128 \\ maximum sequence length & 16 \\ batch size & 64 \\ number of epochs & 10 \\ \hline \end{tabular}
\end{table} 
Table 4: Hyperparameter settings. \begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & \multicolumn{2}{c}{**THUCNews**} & \multicolumn{2}{c}{**TNews**} \\
**Accuracy (\%)** & **F1-Score (\%)** & **Accuracy (\%)** & **F1-Score (\%)** \\ \hline TextCNN & 89.63 & 89.60 & 85.96 & 85.99 \\ BiLSTM-Attention & 90.21 & 90.23 & 85.17 & 85.09 \\ GCNN & 90.48 & 90.51 & 86.12 & 86.05 \\ DPCNN & 90.69 & 90.80 & 86.47 & 86.49 \\ FastText & 90.84 & 90.83 & 86.75 & 86.59 \\ CNN-BiLSTM & 91.26 & 91.34 & 87.22 & 87.23 \\ CNN-BiGRU-Attention & 91.38 & 91.36 & 87.15 & 87.06 \\ Our model & **91.85** & **91.84** & **87.70** & **87.72** \\ \hline \end{tabular}
\end{table} 
Table 5: Comparative experimental results. it obtained 91.85% accuracy and an F1-score of 91.84% for THUCNews, while reaching 87.70% accuracy and an F1-score of 87.72% for TNews. Based on these outcomes, several key insights can be derived:  
1. Our model outperforms other baseline models in terms of both accuracy and F1 score. The experimental results validate that word representations enriched with Term Frequency and POS features provide more-comprehensive semantic cues, and the hybrid model based on a GCNN and BiLSTM can learn more-comprehensive semantic information. 2. The hybrid models CNN-BiLSTM and CNN-BiGRU-Attention outperform other single-architecture models on both datasets. CNN-BiGRU-Attention achieves an accuracy of 91.38% for THUCNews, 1.75% higher than the TextCNN model. CNN-BiLSTM attains an accuracy of 87.22% for TNews, which is 2.05% higher than BiLSTM-Attention. This verifies that the hybrid model's ability to simultaneously capture local features and contextual information can improve classification performance. 3. The GCNN achieves higher accuracy and F1 scores than TextCNN across both datasets, confirming that the introduction of a gating mechanism in the convolutional layers is more effective for extracting key features. The bar charts in Figure 5 further illustrate the performance disparities across categories for the THUCNews and TNews datasets. For the THUCNews dataset, the classification accuracy ranges from 85.3% to 96.8%. Among them, the entertainment category achieved the highest accuracy of 96.8%, whereas the science category had the lowest accuracy of 85.3%. For the TNews dataset, the classification accuracy varies from 81.2% to 93.2%, with the sports and real estate categories achieving the highest classification accuracy at 93.2%. Finance and economics showed the lowest accuracy rates, at 81.3% and 81.2%, respectively.