# Chinese News Text Classification Method via Key Feature Enhancement
## 4 Experiments
### Experimental Results and Analysis  
Experiments are conducted in order to reveal the comprehensive performance of the model from different aspects. Firstly, we conduct comparative experiments using eight different popular classification models on the original dataset #DataSetA. Then, at the macro level, three evaluation indicators (average accuracy, min-loss value and average F\({}_{1}\)-score) are used to complete the final performance evaluation, so as to find the optimal classification model of #DataSetA. The relevant experimental results are shown in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **Avg Accuracy (\%)** & **Min-Loss** & **Avg F\({}_{1}\)-Score (\%)** \\ \hline TF-IDF [23] & 89.26 & – & 85 \\ Naive Bayes [14] & 90.13 & – & 90 \\ SVM [13] & 92.39 & – & 91 \\ FastText [15] & 92.24 & – & 92 \\ LSTM [24] & 93.17 & 0.22 & 93 \\ RNN [17] & 94.49 & 0.21 & 94 \\ TextGCN [25] & 92.78 & 0.24 & 92 \\ CNN [16] & 96.26 & 0.13 & 96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of different classification models on the original dataset #DataSetA-Test. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Confusion Matrix** & \multicolumn{3}{c}{**System Results**} \\  & **Positive (Erroneous)** & **Negative (Correct)** \\ \hline Gold Standard & Positive & TP (True Positive) & FN (False Negative) \\ Negative & FP (False Positive) & TN (True Negative) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Structure of confusion matrix. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Parameter Value** & **Parameter Name** & **Parameter Value** \\ \hline Dropout\_rate & 0.5 & Optimizer & Adam \\ Embedding\_dim & 100 & Seq\_length & 1200 \\ Num\_filters & 256 & Num\_classes & 10 \\ Kernel\_size & 5 & Vocab\_size & 15,000 \\ Hidden\_dim & 128 & Learning\_rate & \(1\times 10^{-3}\) \\ Batch\_size & 64 & Num\_epochs & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyper-parameter settings of the CNN and KFE-CNN model. According to the results in Table 4, it can be seen that the comprehensive performance of the CNN model is better than that of the other seven models in the eight groups of comparative experiments carried out on the original dataset #DataSetA-Test. Its average accuracy value can reach 96.26%, the min-loss value is 0.13 and the average F1-score across all categories is 96%. The comprehensive performance of RNN is second, with an average F1-score of 94% and the average F1-score of other models is between 85% and 93%. According to the above experimental results, we select the CNN skeleton model to build the KFE-CNN model to carry out further comparative experiments on the original dataset #DataSetA and the extended dataset #DataSetB and combine the micro-evaluation indicators between different categories to compute the comprehensive performance of different models. The relevant experimental results are shown in Table 5.  
According to the experimental results in Table 5, it can be seen that the performance of the KFE-CNN model for the enhanced dataset #DataSetB is better than that for the original dataset #DataSetA and a number of comprehensive evaluation indicators have been significantly improved. Compared with the original dataset #DataSetA from a macro perspective, the average accuracy of the KFE-CNN model for the enhanced dataset #DataSetB has increased by 1.58% to 97.84%, the min-loss value has decreased by 0.056 and the average F1-score has increased by 2% to 98%. At the micro level, after the data enhancement processing, the comprehensive performance of the KFE-CNN model's evaluation indicators in different categories has been significantly improved. Among them, only the precision indicators of the two categories of #Home Furnishing and #Fashion have dropped by 2%, but they are all above 95%. There is a significant increase in all remaining categories, especially #Education, which sees an 8% improvement. Recall and F1-score have different improvements in all categories or are the same as the original dataset.