## 4 Discussion  
From examining Table 4, it can be seen that there are two notable high-performing models on this dataset from the simplest and most complex algorithms tested. The simplest algorithm (Naive Bayes using dataset variation F) achieved 85.1% accuracy on the test dataset, using a jieba word-level CountVectorizer with 84,025 unicode tokens. This model size was 15 MB, does not require a GPU, took a total of 45 s to train the vectorizer, and the model can predict 3,625,000 headlines per second. The most complex algorithm (NN BERT) achieved 89.3% accuracy using the hyperparameters of \(2\times 10^{-5}\) learning rate, batch size of 32, and 2 epochs of training. The model size was 409 MB, and training the vectorizer and model took 2.1 hours with an NVIDIA T4 GPU. The model could predict 195 headlines per second.  
There are vast differences in time and computing resources between the two best algorithms, as the BERT model required 170x more time to train, was slower to predict by a factor of 18,600, and required 27x more disk space to save the model. This model could be the best choice for a low-volume application when the highest accuracy is needed. For applications on a larger scale where a slight degradation in performance can be tolerated, the Naive Bayes algorithm could be the best choice.  
The detailed model performance of the tuned BERT algorithm is presented in Figure 10, with the confusion matrix on the left and the classification report on the right. The model performed fairly consistently on the test/holdout dataset across all categories, with the exception of the world category, which possessed F1 = 77.6%. On the left side of the figure, the model had the highest level of confusion between the military and world categories and between the finance/technology and car categories.  
Figure 9: Training curves for the TF-IDF sequential neural network model for the character-encoded model (**a**) and the word-encoded model (**b**). Accuracy was calculated using the training dataset (blue lines) and validation dataset (orange lines).  
Considering the high volume of Chinese language articles posted online on any given day that a typical Chinese researcher needs to process, deploying this model in a web scraper could assist in the initial selection of articles for analysis.  
Web scrapers are easy to build and deploy and are commonly used by researchers and journalists to automate the collection of news articles from online sources. They are cheap and effective to use but often use exact keyword matching to select articles for retention. Keyword matching can grab unintended articles and miss articles of interest. Keyword lists also require maintenance to keep up with changes in keywords. Applying the models developed in this work would reduce the number of extraneous articles collected, increase the collection of articles of interest, and eliminate the need to maintain keyword lists. This would result in overall savings by allowing the web scraper to complete its task more efficiently and in less time and enabling researchers to focus on the most relevant articles. This method could also reduce computer memory storage requirements as it selects a higher number of relevant articles and fewer unnecessary articles. It would also enable researchers who are not language-enabled to effectively use the scraper, allowing language-enabled researchers to focus efforts elsewhere.