## 2 Materials and Methods
### Natural Language Processing  
Simplified Chinese Mandarin characters dominated the dataset, with only a small number of headlines in traditional characters. In Chinese Mandarin, the meanings of traditional and simplified characters are generally the same, so training only needs to happen on one character set if the traditional characters are converted to simplified. Written dialects of Chinese, such as Cantonese, were not in the dataset and, therefore, were not considered in this study.  
Initial data processing created the stop words list and segmented the headlines [21]. In NLP, stop words are words, characters, or symbols in a text that typically convey little meaning but may perform a grammar function or other purpose and that are generally considered unimportant when labeling the data [21; 22]. Stop words tend to rise toward the top of frequency lists of words as they are often the most common elements of a corpus and are removed from a text before it is processed by a model. Stop word removal was part of the pre-processing for each model. A custom-built set of 83 stop words was determined by examining the frequencies of characters and words in the training set. The stop word list contained English words such as the/and/is, individual Chinese characters, Chinese \"words\" created from the jieba encoder, and common punctuation marks. All algorithms used this stop word list except for BERT, which did not use stop words. As a transformer model, BERT derives context from the surrounding words, and excluding stop words could hurt performance. After an analysis of the frequencies of characters and words in the training set, candidate stop words were selected and written in a text file.  
The 382,688-entry dataset contained both English and Chinese, and the number of English-only headlines was found to be minuscule -- all categories had 2 or fewer English-only headlines except for one 36-count category. As a result, headlines with English words were retained. Stemming and lemmatization are common NLP steps that deconstruct a word to arrive at the shared meaning in similarly spelled words since it is the meaning that should be modeled with the other words in the text. Stemming is a relatively simple process that truncates the end of a word; for example, process, processed, and processing would all be reduced to process. Lemmatization is a more complex process that addressesplurals, tenses, and gerunds in order to reduce the word to its base form, also known as a lemma. Chinese does not have these elements, so stemming and lemmatization were not required [21].  
The vocabulary provided to the models, also known as the set of unique tokens, was evaluated by character-level and word-level approaches. The character-level vocabulary had a size of 5888 characters. It represented all of the characters used throughout the headline corpus and could be used because of the way each Chinese character retains a meaning that is often similar, regardless of the surrounding characters. After applying jieba to the training corpus, the word-level vocabulary had 167,506 words. Limiting the size and selection of these vocabularies provided an option for tuning the models, and models were built ranging from 25 words in increments to the maximum vocabulary [11]. Vectorization and feature selection occurred via _sklearn_ Count Vectorizer for bag-of-words (word count) encoding, _sklearn_ TF-IDF for statistical TF-IDF encoding, _keras_ TextVectorization for NN TF-IDF encoding, and BertTokenizer for the BERT models. The NLP processing steps varied per algorithm, and a summary is provided in Table 3. More details on each algorithm are provided in Section 2.3.