## Empirical Study
### Comparison and Discussion  
To enhance the efficiency of a big textual data optimization processing, this section uses the corpora of COVID-19 news reports as an empirical example to discuss the differentiations of the proposed approach in three aspects that include optimization efficiency, refined results, and knowledge extraction. Firstly, for function words elimination and machine elimination of function words, the proposed approach is compared with three listing approaches to explore the refining efficiency. Secondly, the differences between the original and refined data are quantitatively presented to verify the proposed approach. Finally, there is information retrieval (IR) from a big textual dataset to highlight the significant values of knowledge extraction. Detailed descriptions are shown as follows. (1) Function words elimination and machine elimination of function words  
This paper compares four ESP cases (see Table 7). Firstly, the authors compare the elimination of function words. [PERSON]'s approach [29] was used to explore the linguistic and domain usages of vague terms in JRC-Acquis (EN), a corpus of legal documents. Thus, the study did not eliminate function words manually or by machine processing. Vague terms, in [PERSON]'s study [31], were listed as _some, or more, several, about, a period of, a number of,_ and so on; those terms frequently collocated with quantifiers, prepositions, and other function words. If function words were deleted, the corpus program might unable to cluster vague term phrases for linguistic analysis. That is, for some cases in genre or general linguistic analysis, function words are essential because they are critical components for constructing English clauses and sentences. The proposed approach, [PERSON]'s approach [32], and [PERSON] and [PERSON]'s approach [33] focused on analyzing specific cases of COVID-19 news reports, engineering fields, and President [PERSON]'s tweets, respectively; thus, function words needed to be eliminated because specific words or terms were more relevant for retrieving domain knowledge and critical information from the target corpora. Secondly, the researchers compare machine-based means for eliminating function words. [PERSON]'s approach [30] created a manual five-stage filtering approach to refine the data (i.e., the keyword list) resulting from the corpus software in the context of retrieving opaque words in engineering corpora. The first two steps in the manual five-stage filtering approach were removing meaningless words and function words. [PERSON]'s approach [32] did not use a machine (e.g., a corpus software or other computer method) for deleting function words. If the proposed approach were embedded into [PERSON]'s approach [30], it would save two steps in the manual five-stage filtering process and enhance the efficiency of the wordlist analysis. [PERSON] and [PERSON]'s approach [33] simply removed function words from the keyword list manually after the corpus software created it and then made a taxonomy of keywords. [PERSON] and [PERSON]'s approach [33] did not use a machine to remove function words either. In the first part of this discussion, this paper demonstrated the discrepancy between the original keyword list and the refined keyword list. The refined keyword list in this study increased the coverage of keywords. If the proposed method were embedded into [PERSON] and [PERSON]'s approach [33], it would enhance the breadth of the keyword list and save the step of eliminating function words manually. That is, [PERSON] and [PERSON]'s approach [33] would no longer need to consume time in manually adjusting \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Function Words Elimination** & **Machine Eliminates Function Words** \\ \hline [PERSON]’s approach [29] & No & No \\ [PERSON]’s approach [30] & Yes & No \\ [PERSON] \& [PERSON]’s approach [31] & Yes & No \\ The proposed method & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 7: A comparison of research methods in ESP cases.