## Empirical Study
### Comparison and Discussion
the data resulting from the corpus software; instead, the proposed approach might allow them to reveal more accurate data and conduct a token taxonomy directly. The proposed method used a machine-based technique to eliminate function words and meaningless words in order to augment the efficiency and accuracy of the data resulting from corpus analysis. (2) Data discrepancy  
There is a significant discrepancy between the original data and refined data in both the wordlist and keyword list. In terms of the wordlist (see Table 8), the refined data (word types: 16,536; tokens: 457,891) eliminated 211 function words compared to the original data (word types: 16,325; tokens: 234,612), the size of the corpora was dramatically reduced by 48.8%, and the TTR raised from 3.6% to 7%. The remaining 51.2% of the corpora contains words that are more meaningful and domain-specific. This confirms that function words are critical elements for constructing English sentences; it also confirms that the proposed approach significantly improves corpus analysis efficiency by machine processing rather by manual optimization. Moreover, only 211 word types occupy 48.8% of the target corpora. It is no wonder that function words do cause some inconvenience and interference in processing information in ESP cases. Keyword extraction is designed for filtering out function words, unrelated words, or commonly-used words in the target corpora. However, even if the corpus software has sophisticated statistical algorithms, the function words can still interfere with the resulting data in the keyword list. According to Table 9, the proposed approach makes corpus software recalculate the keypess of each token: the refined data (keyword types: 2149; tokens: 156,841) as compared to the original data (keyword types: 1346; tokens: 226,271) increased 803 keywords and reduced 69,430 tokens (downsizing 30.7%). The keyword list increase of 803 keywords makes keyword analysis more accurate and embeds more detailed information for analyzing ESP cases. (3) Demonstration of knowledge extraction  
Knowledge extraction from a big textual data is demonstrated by taking _coronavirus_ as an example. According to the refined wordlist and keyword list, _coronavirus_ is ranked first based on both its frequency (=2425) and keypess (=17,780.87). However, before the wordlist was refined, the original wordlist showed _coronavirus_ is ranked 24 th; that is, the top 23 functional tokens (such as _the, to, and, of,_ etc.) must be eliminated or filtered manually or automatically for _coronavirus_ to emerge for further ESP analysis. Here, the researchers categorize significant features of the linguistic patterns of _coronavirus_ using the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{4}{c}{**COVID-19 News Reports Corpora**} & **Data Types** & **Word Types** & **Tokens** & **TTR** \\ \hline
**Year** & **Month** & **Numbers of Reports** & & & \\ \hline \multirow{4}{*}{2020} & January \& & Original & \multirow{2}{*}{16,536} & \multirow{2}{*}{457,891} & \multirow{2}{*}{3.6\%} \\  & February & & data & & \\ \cline{1-1} \cline{  
corpus software platform and extracting information about _coronavirus_ from the big textual data of the COVID-19 news reports. Firstly, the researchers check the Cluster/N-Grams to find linguistic clusters of _coronavirus_ and, based on the researchers' information analysis intentions, extract six critical types of information from the top 30 clusters list (see Table 10). Then, the researchers check the concordance lines of each cluster (see Figure 6) based on Table 10 in order to retrieve crucial information. **Example 1. **_coronavirus outbreak_: the cluster of _coronavirus outbreak_ can be considered as an index to assist in searching for the best related content from the target corpus, such as events related to the disease outbreak (1-1, 1-2), the number of infected and deaths (1-3, 1-4, 1-5), and so on. 1. (retrieved from FOX-Jan-26-5) _Coronavirus outbreak_ _spurs Paris to cancel Lunar New Year parade..._
2.