A Novel Statistic-Based Corpus Machine Processing Approach to Refine a Big Textual Data: An ESP Case of COVID-19 News Reports

Abstract

With developments of modern and advanced information and communication technologies (ICTs), Industry 4.0 has launched big data analysis, natural language processing (NLP), and artificial intelligence (AI). Corpus analysis is also a part of big data analysis. For many cases of statistic-based corpus techniques adopted to analyze English for specific purposes (ESP), researchers extracted critical information by retrieving domain-oriented lexical units. However, even if corpus software embraces algorithms such as log-likelihood tests, log ratios, BIC scores, etc., the machine still cannot understand linguistic meanings. In many ESP cases, function words reduce the efficiency of corpus analysis. However, many studies still use manual approaches to eliminate function words. Manual annotation is inefficient and time-wasting, and can easily cause information distortion. To enhance the efficiency of big textual data analysis, this paper proposes a novel statistic-based corpus machine processing approach to refine big textual data. Furthermore, this paper uses COVID-19 news reports as a simulation example of big textual data and applies it to verify the efficacy of the machine optimizing process. The refined resulting data shows that the proposed approach is able to rapidly remove function and meaningless words by machine processing and provide decision-makers with domain-specific corpus data for further purposes.

1. Introduction

In this era of booming information and communication technologies (ICT), Industry 4.0-oriented manufacturers have widely developed artificial intelligence (AI) technology for reducing manual operations and increasing machine production ratios, which decreases production costs and improves production efficiency [1,2,3]. For example, Nicolae et al. [4] discovered current systems based on the Industrial Internet of Things (IIoT) concept to process the data of water industries, drinking water treatment plants (DWTP), seem inadequately intelligent to reduce costs and to be utilized in quality controls. Thus, in their research, the developed algorithm is dedicated to making systems smarter and more comprehendible in processing the data. Moreover, Sung et al. [5] created the collection algorithm via utilizing a collection box; their invented algorithm based on an experimental design method embedding multiple sensors was able to be utilized in handling the current collection problems and decreasing logistics costs. To successfully integrate human intention with the activity of machines, related tasks such as big data and big textual data analysis, systems integration, and rapid data flow between systems all depend on importing advanced algorithms and natural language processing (NLP) techniques [6,7,8]. In terms of big data computing, based on the differences between its information processing techniques and data varieties, it can be divided into batching big data computing [9] and streaming big data computing [10]. For big data analysis utilized in medical industries, batching big textual data studies that adopt corpus-based approaches and NLP techniques [11,12,13] are commonly seen for processing electronic medical records and medical information, managing medical resources, assisting in diagnosing, and so on. Corpus-based approaches are also widely utilized by many researchers for specialized information analysis and integration [14,15,16,17,18,19]; these approaches use statistic algorithms to process natural languages (NLs) in order to retrieve critical information, hidden linguistic patterns, domains knowledge, etc. from big textual data. However, no matter how powerful ICT processing equipment is, when facing linguistic meanings behind NLs they are sometimes still helpless [20]. Thus, to make machines understand humans’ commands and to integrate ICT technologies, optimizing processes that use mathematic algorithms and linguistic theories must be implemented and valued constantly [21,22,23].

There are various corpus-based approaches for analyzing big textual data [24,25]. For instance, Vianna et al. [26] used a type of corpus-based approach to conduct a systematic literature review (SLR) for identifying the role of crowdsourcing in Industry 4.0. Carrion et al. [27] used a semi-automated taxonomy-generating toolkit to support field specialists in establishing taxonomies of terminologies and in creating semantic illustrations of the target corpus. Their method was able to automatically retrieve, reorganize, and classify domain-specific tokens and enable field specialists to participate in processing procedures. In terms of typical corpus-based approaches, corpus software such as Wordsmith Tools [28], KeyBNC [29], and AntConc 3.5.8 [30], are popularly applied in investigating linguistic patterns from big textual data. Li [31] adopted Wordsmith Tools [28] to define the purposes of vague terms to increase the applicability and flexibility of legal documents. The results of linguistic analysis could provide lawmakers with critical reference factors for crafting laws, legal regulations, and principles in the future. Todd [32] adopted KeyBNC [29] to generate keyword lists, to find tokens’ dispersions, and to test log-likelihood values for identifying opaque engineering word lists from big textual data of engineering textbooks in order to optimize pedagogical qualities. Ross and River [33] used FireAnt [34], a program used for Twitter data gathering, and AntConc 3.5.8 [30] to process US President Donald Trump’s tweets in order to identify President Trump’s propaganda strategies during his first presidential election.

Generally speaking, the purposes of corpus-based approaches are (1) simplifying NLs’ complexities and (2) extracting critical information (i.e., linguistic evidence) from big textual data. The formations of English lexical units are divided into function words and content words. Function words such as the, to, is, etc. are essential elements for constructing sentences grammatically; content words are verbs, nouns, etc. that embed substantive meanings, such as move, apple, beautiful, etc. In many English for specific purposes (ESP) cases, function words are mostly not essential parameters for researchers exploring linguistic patterns of English in specialized usages. Therefore, keyword list generation via corpus software is a mechanism to filter out function words and extract more domain-oriented words. However, there are still some doubts about the calculation of ‘keyness’ (e.g., benchmark corpus selections may cause different results), so many researchers choose to eliminate function words manually to refine wordlist or keyword list data [32,33]. Still, manual annotation is an inefficient approach for big textual data analysis, and it more easily causes flawed results and information distortions. In this paper, the researchers propose a novel machine-optimizing corpus-based approach to refine big textual data. The approach is more suitable than other approaches for processing information related to the explosion of the recent novel coronavirus disease. It not only adopts a statistic-based corpus software to process big textual data but also uses software to remove function and meaningless words instantly to greatly improve the efficiency of corpus analysis.

Since late December 2019, a novel atypical pneumonia named COVID-19 has infected over 3 million people internationally and caused over 200,000 deaths; in addition, the numbers of confirmed cases and deaths are constantly raising. COVID-19 is described as the biggest crisis that people have faced since World War II [35]. Huge amounts of information related to COVID-19 are chaotic and much of it is disputed; moreover, false news or misleading information keeps emerging and flowing between people [36]. Facing such huge amounts of textual information, the efficiency of big data analysis determines the speed of information integration and information application; moreover, it decides whether people can reach a dominant position in epidemic prevention and control, disease knowledge acquisition, and database establishment [37]. Before the World Health Organization (WHO) named COVID-19, the novel coronavirus was called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by medical experts, described as infecting humans and causing atypical pneumonia [38,39]. Yang et al. [40] claimed that three critical elements of disease infection are sources of infection, suitable receptors (e.g., people with poor immune systems), and transmission routes. However, the infectious sources of COVID-19 are still a mystery. When investigating initial groups of infectious cases, scientists found most of the patients had visited Huanan seafood wholesale market, and many wild animal corpses such as bats, pangolins, rodents, and snakes were found there. Those wild animals were identified as the potential hosts of COVID-19 [41,42]. Once viruses that are parasitic in animals enter human-to-human transmission routes, human patients become the largest sources of infection. The genetic sequence of COVID-19 was turned in to GenBank in January 2020, and it was given the serial number MN908947 [43]. COVID-19 has over 70% similarity in genetic structure compared to SARS-CoV (the severe acute respiratory syndrome that emerged in 2002) [44,45]. However, Wan et al. [46] observed that the spike (S) protein discovered on COVID-19 had mutated, and the mutated parts of COVID-19 may decrease the resistant capabilities of patients’ immune system. That is, the S-protein mutation may be the reason that COVID-19 causes faster spread than SARS-CoV. The implementation of proper quarantine and disease control and prevention policies is also crucial during COVID-19 pandemic periods. Research by Brown and Pope [47] confirmed that COVID-19 can be transmitted by airborne routes; that is, tiny droplets under 5 lm floating in the air can be inhaled by people and cause infection in the respiratory system. When the virus stays on the surfaces of objects, people may also get sick by touching the contaminated objects [38]. Thus, many countries’ Centers for Disease Control and Prevention (CDC) have recommended that frontline medical personnel be equipped with respirators when in contact with and treating COVID-19 patients [47]. In addition, Kim et al. [48], using the sudden COVID-19 pandemic in South Korea in March 2020 as an example, discussed suitable and rapid testing toolkits or approaches to diagnose confirmed cases in order to facilitate quarantines and implement effective disease control.

In facing COVID-19 pandemic control and prevention, big textual data analysis is also indispensable. Although the transmission rate of COVID-19 infection is very high, COVID-19 is not a 100% fatal disease; in fact, medical studies suggest that the lethality rate of COVID-19 is less than 10% [49,50]. False news and messages of panic fill our daily lives during the COVID-19 pandemic [51]. Therefore, the method proposed in this paper refines corpus data from COVID-19 news reports to enable researchers to instantly acquire domain-oriented words and to then obtain objective, scientific, and positive core information from the big textual data. The proposed method is divided into 4 phases. Phase one includes the preparatory work of corpus compilation and selecting proper benchmark corpus. Phase two is generating the raw resulting data of an original wordlist and original keyword list from the corpus software. Phase three is the optimizing process, which embraces manual setting and machine processing. Phase four is generating refined data that embraces a refined wordlist and keyword list from the corpus software. The refined results provide critical information and important reference data for linguists, domain experts, and decision-makers.

The remainder of this paper is briefly described as follows. Section 2 is a literature review and explains the academic terminologies. Section 3 probes into and illustrates detailed steps of the proposed method. Section 4 uses COVID-19 news reports as the target corpora and as an empirical example to verify the proposed method. Section 5 is the concluding part of this study.

2. Literature Review

2.1. Corpus Analysis

Corpus analysis is a field of linguistic analysis research that was introduced with the invention of computers in modern times. Texts could be retrieved from internet resources, books, discourse records, video transcripts, etc. and saved on computers. To deal with the large amount of linguistic data, applying NLP by importing mathematical statistical models is already the norm. The Hidden Markov Model (HMM), decision tree, tree bank, and linear regression algorithms are the most commonly adopted for processing NLs and developing corpus software or NLP techniques. Dunning [52] proposed the likelihood ratio method to calculate the interrelations between tokens, which became an important statistical algorithm for the development of corpus software. It enables the calculation of the ‘keyness’ of tokens in order to generate keyword lists. Dunning’s likelihood ratio method is defined as follows:

Definition 1 [52]. 

Let two discrete random variables$X_1$and$X_2$have a binomial distribution.$n_1$and$n_2$are the numbers of trials, respectively.$p_1$and$p_2$are the success probability of a single trial.$k_1$and$k_2$are the total number of successes, respectively. Then, the logarithm of the likelihood ratio for the binomial distribution will be constructed as:

$$-2\log \lambda =2\left[\log L\left(p_1,k_1,n_1\right)+\log L\left(p_2,k_2,n_2\right)-\log L\left(p,k_1,n_1\right)-\log L\left(p,k_2,n_2\right)\right]$$

(1)

where

$$L\left(p,k,n\right)=p^k{\left(1-p\right)}^{n-k}$$

(2)

$$p_1=\frac{k_1}{n_1},p_2=\frac{k_2}{n_2},\mathrm{and}p=\frac{k_1+k_2}{n_1+n_2}.$$

(3)

Corpus software, also known as a concordancer, is mostly based on statistic algorithms that reorganize tokens from the big textual data. Functions of corpus software include word frequency computing, keyness calculation, clusters/n-gram creation, text dispersion identification, plot calculation, and the provision of concordance lines; those functions integrate statistic models, providing linguists with optimal resolutions in investigating linguistic evidence. O’Keeffe et al. [53] listed several applications of corpus analysis: (1) lexicography, which indicates the big textual data (i.e., corpus or corpora data with over a million words) analysis exams’ tokens’ frequency, collocations, syntaxes, semantic usages offering lexicographers novel approaches in establishing contemporary and updateable dictionaries or database, (2) grammar, which indicates corpus analytical tools that provide linguists with genuine and modern concordance lines for probing detailed and broad views of linguistic evidence such as grammatical patterns and rules and interrelationships between lexicons, (3) stylistics, which indicates how corpus analysis could provide series linguistic empirical evidences for defining the genres of literature and conducting literary comparison, (4) translation, which indicates that corpus analysis can be used for optimizing the results of human and machine translation, (5) forensic linguistics, which indicates the ways corpus analysis is broadly used in legal domains for preventing and investigating crime incidents, investigating offensive and defensive debates in courts, and retrieving knowledge from law data or legal documents by collecting and analyzing legal documents, courtroom data, and other related sources, (6) sociolinguistics, which indicates that corpus analysis is also adopted for investigating linguistic usages of different variants (i.e., people’s social and economic statuses, levels of education, racial variations, regional variations, and so on).

To sum up this section, many corpus-based approaches, especially analytical software, are developed with the assistance of mathematical statistical models and are designed to aid linguists in interpreting syntax and semantics and in identifying linguistic patterns. Corpus analysis can also be carried out from qualitative and quantitative angles.

2.2. COVID-19

Since the end of December 2019, there has been an outbreak caused by a novel type of coronavirus originating in the city of Wuhan, China. The novel coronavirus has spread among people with frightening speed, causing a global pandemic [54]. After confirming more than 1000 diagnosed cases in China domestically, Chinese officials decided to lock down Wuhan to implement medical quarantine measures in order to prevent further spread of the virus. These quarantine policies seem to have effectively controlled and responded to the spread of the virus in China, according to Chinese officials’ claims; however, they have not seemed to have much effect on international disease prevention and control. By early April 2020, there were over 3 million confirmed infectious cases worldwide, which includes over 200,000 deaths (see Figure 1); moreover, the novel coronavirus has severely and constantly impacted nations’ political and economic systems globally. Although COVID-19 is a novel type of coronavirus, its genetic features are similar to SARS-CoV and MERS-CoV (Middle East Respiratory Syndrome, an outbreak of which occurred in 2012) [41,49], but COVID-19 has stronger infectivity and lower lethality than these previous two viruses [40]. In the early days of the COVID-19 epidemic, most news reports and internet resources referred to it as the Wuhan coronavirus, Wuhan pneumonia, etc. Those targeted and discriminatory terms sparked Chinese officials’ dissatisfaction. In February 2020, the WHO defined the nomenclature for this novel coronavirus as COVID-19: the CO was extracted from corona, the VI from virus, and the D from disease, and the 19 indicates that the disease was found in 2019. Furthermore, the WHO claimed that the world should actively fight the pandemic and eliminate discrimination towards China.

There is no definite source of the COVID-19 infection, but in the initial investigation of confirmed cases in Wuhan, experts found that more than 80% of patients had visited Huanan seafood wholesale market. After entering the market, the medical experts and scientists found that wild animals such as bats, snakes, birds, and pangolins were sold for cooking; local people call those kinds of meats “Yeh-Wei,” which means wild ingredients, and those meats are popular in Chinese food culture. Therefore, most scientists and epidemiologists speculated that COVID-19 had mutated in wild animals’ (e.g., bats, pangolins, etc.) immune systems and entered human-to-human infectious routes when people processed the corpses or ate undercooked meats. However, there were other experts who believed that the biosafety level 4 laboratory in Wuhan had failed to take protective measures during experiments and caused virus leakages. The sources of information are intricate and controversial, and no definitive answers have been found. Li et al. [55] pointed out that most COVID-19 patients’ clinical symptoms were similar to influenza but more like an atypical pneumonia; furthermore, COVID-19 can cause severe damage to patients’ respiratory systems (see Figure 2). According to an analysis of 1560 medical cases by Li et al. [55], the mortality rate of COVID-19 is approximately 5%. COVID-19 is not obviously detectable during its incubation period and can be spread through droplets and human contact before people know they are infected; its strong spreading capabilities increase the difficulty of pandemic prevention and control.

The COVID-19 outbreak, due to a series of medical quarantines and disease control measures, has made a tremendous impact on industries such as aviation, transportation, and tourism. In order to effectively control the spread of COVID-19, many countries adopted policies to lock down cities and close national borders, causing the cancellation of countless flights and tourist trips. In addition, many companies have released employees to work at home, and many factories have had to stop production and wait for the pandemic to ease. COVID-19 has even spread in the military of many countries, affecting soldiers’ health and decreasing military combat capabilities (e.g., the COVID-19 incident onboard the USS Theodore Roosevelt). That is, COVID-19 has had an immense impact on national political systems, endangered national security, and caused unimaginable losses in the global economy.

3. Methodology

Many corpus-based research studies may face a scenario in which the resulting data includes many function words and meaningless words, which increases difficulties in data analysis. Although many researchers apply a keyword list generating function to filter out function words and extract domain-oriented words, manual refining processing is still considered unavoidable [32,33]. Manual refining processing is a slow and labor-consuming process. In addition, resulting data may contain biases because researchers have different perceptions and interpretations when processing the data.

The COVID-19 outbreak has recently hit and ravaged the world. COVID-19 infects people at an alarming rate and has also severely impacted the global economy. No direct and effective medical treatments have been identified yet to fight the disease; quarantines and self-protective measures such as decreasing group gatherings, wearing masks, or improving autoimmunity are currently our best weapons to fight COVID-19. COVID-19 has received worldwide attention: explosive information such as news reports, medical reports, and scientific discoveries, are rapidly collected and processed to unveil clues or mysteries about the disease. Moreover, copious international news information spreads out to notify and warn people to raise their alertness.

To improve analytical efficiency and the speed of concordance generation in processing text information, the researchers propose a novel machine-optimizing corpus-based approach to refine the big textual data from the AntConc 3.5.8 [30] program. The researchers established a function word list and embedded it into the program, in order to refine the wordlist and keyword list and enhance the efficiency of corpora processing. The proposed method is divided into four phases and includes a total of nine steps (see Figure 3). Phase 1 comprises steps 1 and 2 and is considered preparatory work. Phase 2 includes steps 3 and 4 and generates raw data. Phase 3 consists of steps 5 and 6 and is considered the optimizing process. Phase 4 includes steps 7 and 8 and generates refined data. In addition, step 9 indicates the future purposes of the refined results from phase 4. Detailed descriptions of each step are provided below.

Step 1. Corpora compilation

A corpus can be compiled from written or spoken texts on the internet or from diverse sources, but users must pay attention to and respect international copyrights. The gathered corpora can be run through concordancers such as Wordsmith Tools [28] or AntConc 3.5.8 [30] to conduct corpus analysis. In this paper, the researchers use the AntConc 3.5.8 [30] program to conduct corpus analysis because it is free and easy to access. All texts must be transformed into a .txt (UTF-8) format to be compatible with the concordancer.

Step 2. Selecting and inputting the benchmark corpus

Keyword list generation relies on the comparison of two existing corpora (a target corpus and a benchmark corpus). The corpus software relies on its algorithm (e.g., log-likelihood test) to compare the two corpora and calculate the keyness of tokens for generating a keyword list. Usually, a benchmark corpus is a larger corpus and provides background corpus data for referential comparison. In this study, the researchers choose the corpus of contemporary American English (COCA) as the benchmark corpus and input it into the AntConc 3.5.8 [30] program to generate a keyword list.

Step 3. Generating a wordlist from the target corpora

Once the target corpora have been inputted, generate a word list on the AntConc 3.5.8 [30] program without any additional adjustments.

Step 4. Generating a keyword list from the target corpora

The premises of generating a keyword list are importing the benchmark corpora and generating a word list of the target corpora. After completing these tasks, researchers generate a keyword list using the AntConc 3.5.8 [30] program without any additional adjustments.

Step 5. Establishing a function word list

This paper reviews related English linguistic knowledge and check with native English speakers to identify the most frequently used English function words, and compiles those function words as a function word list.

Step 6. Limiting wordlist range

In this step, a function word list will be added into the “word list preferences function” of the AntConc 3.5.8 [30] program. Researchers choose “word list range”, open the list file, and select “use a stop list below” and then “apply” the settings (see Figure 4) to limit the ranges of the word list and keyword list from the target corpora.

Step 7. Generating a refined word list from the target corpora

After completing the settings in Step 6, researchers generate a word list from the target corpora again.

Step 8. Generating a refined keyword list from the target corpora

After the refined word list is generated, researchers generate a keyword list from the target corpora again.

Step 9. Providing critical information and reference data for decision-makers

4. Empirical Study

4.1. Overviews of the Target Corpora

COVID-19, a novel coronavirus disease, has been raging in countries worldwide since December 2019. By the end of April 2020, COVID-19 infected more than 3 million people and caused over 200,000 deaths; the increasing number of confirmed cases has not stopped but is growing at an incredible speed. The American mainstream media raised the severity of COVID-19 from an “epidemic” to a “pandemic.” COVID-19 has severely impacted and upset the national political, economic, and medical systems. Reports related to COVID-19 spread explosively; thus, the efficiency of big data processing will determine whether people can achieve a dominant position to understand and fight the virus.

In this study, researchers collected news reports from the FOX News website as the big textual data. The corpora comprise of 615 news reports that focused on COVID-19 from 1 January to 29 February. To verify that the data resulting from the corpus analytical program is optimized by the proposed method, the researchers choose the corpora of COVID-19 news reports as an empirical example on which to implement the four-stage optimizing procedure.

4.2. The Proposed Method

Step 1. Corpora compilation

The target corpora contain 615 COVID-19 news reports from the FOX News website, one of the mainstream media organizations in the United States. The corpora are divided into Corpus 1 and 2, containing news reports from January and February, respectively; the corpora consist of 16,536 word types and 457,891 tokens, and their diversity indicator—type/token ratio (TTR) is 3.6% (see

Table 1. Quantitative characteristics of the target corpora.

FOX News Corpora				Word Types	Tokens	TTR
Sub-Corpora	Year	Month	Numbers of Reports
Corpora 1	2020	January	159	9938	173,531	5.7%
Corpora 2	2020	February	456	13,513	284,360	4.8%
Whole corpora			615	16,536	457,891	3.6%

and Figure 5).

Step 2. Selecting and inputting the benchmark corpus

To generate a keyword list from the target corpora, the researchers choose the corpus of contemporary American English (COCA) as the benchmark corpus for input into AntConc 3.5.8 [30]. In this study, the COCA sample, recently released online, encompasses 123,029 word types and 9,412,521 tokens, and its TTR is 1.3%. The benchmark corpus is much larger than the target corpora and genre-balanced; thus, COCA can be considered an ideal benchmark corpus.

Step 3. Generating a wordlist from the target corpora

The corpora of COVID-19 news reports are analyzed using AntConc 3.5.8 [30], and the wordlist is generated without any additional adjustments. The raw wordlist covered 16,536 word types and the words were ranked in frequency order. An example of the wordlist (see

Table 2. An example of the word list from the input corpora (partial data).

Rank	Freq.	Tokens	Rank	Freq.	Tokens	Rank	Freq.	Tokens
1	26,375	the	35	1767	who	69	905	out
2	13,479	to	36	1585	virus	70	905	which
3	11,042	and	37	1551	all	71	880	no
4	10,138	of	38	1531	been	72	845	like
5	8978	in	39	1449	an	73	844	would
6	8404	a	40	1412	about	74	843	other
7	6899	that	41	1396	there	75	840	know
8	6276	s	42	1332	will	76	829	after
9	5038	is	43	1323	so	77	829	than
10	4378	it	44	1319	president	78	796	going
11	4259	for	45	1304	by	79	786	over
12	3821	on	46	1295	what	80	764	up
13	3561	i	47	1292	health	81	746	our
14	3319	you	48	1258	or	82	732	some
15	3186	have	49	1227	more	83	724	officials
16	2916	this	50	1198	TRUMP	84	720	because
17	2805	we	51	1184	new	85	717	its
18	2736	with	52	1182	cases	86	704	don
19	2707	he	53	1114	one	87	674	right
20	2699	are	54	1098	were	88	669	WUHAN
21	2586	they	55	1076	outbreak	89	666	reported
22	2531	from	56	1049	can	90	664	country
23	2432	was	57	1034	if	91	664	first
24	2425	coronavirus	58	1023	u	92	663	when
25	2419	as	59	1014	their	93	656	FOX
26	2386	be	60	1009	had	94	654	how
27	2354	said	61	998	re	95	652	she
28	2279	at	62	995	his	96	637	well
29	2225	CHINA	63	982	do	97	620	two
30	2160	not	64	977	now	98	616	them
31	2155	has	65	969	news	99	613	CHINESE
32	1936	people	66	967	think	100	609	time
33	1859	but	67	926	also
34	1808	t	68	906	just

) shows many function words, meaningless words, or even characters embedded in the wordlist (words were highlighted in Table 2). It decreases the efficiency of analysis of the wordlist if researchers must make manual corrections or eliminations.

Step 4. Generating a keyword list from the target corpora

Keyword list generation is a function of corpus software for filtering out function words, meaningless words, and words that especially in general purposes. Corpus software uses likelihood ratio algorithms to determine the keyness of each token. Keywords are considered words that have specific purposes and will indicate the domain characteristics of the target corpora. According to

Table 3. An example of the keyword list from the input corpora (partial data).

Rank	Freq.	Keyness	Keywords	Rank	Freq.	Keyness	Keywords
1	2425	14,637.48	coronavirus	51	561	968.67	according
2	2225	9515.52	CHINA	52	218	963.68	flu
3	1585	8708.51	virus	53	1936	961.93	people
4	1076	6230.89	outbreak	54	156	958.1	HUBEI
5	1198	4354.6	TRUMP	55	191	950.62	BOLTON
6	669	4082.42	WUHAN	56	182	940.26	doesn
7	1182	4078.78	cases	57	320	934.19	February
8	1292	3074.65	health	58	185	845.64	respiratory
9	704	2876.85	don	59	206	843.84	amid
10	566	2869.1	infected	60	245	841.06	illness
11	502	2844.33	CDC	61	259	803.62	tested
12	577	2507.33	confirmed	62	351	798.05	democrats
13	656	2435.6	FOX	63	220	797.32	airlines
14	445	2213.44	CARLSON	64	221	786.84	prevention
15	1319	2196.98	president	65	189	779.76	province
16	724	2030.99	officials	66	135	773.99	epicenter
17	969	1944.92	news	67	208	737.75	KOREA
18	415	1938.04	flights	68	2155	728.95	has
19	327	1926.38	quarantine	69	181	723.5	princess
20	573	1877.98	travel	70	268	703.85	Thursday
21	613	1793.13	CHINESE	71	193	688.55	witnesses
22	345	1766.64	impeachment	72	180	688.1	reportedly
23	523	1763.09	spread	73	123	685.38	pandemic
24	666	1751.98	reported	74	372	684.73	medical
25	543	1645.35	disease	75	416	682.33	video
26	390	1605.22	passengers	76	144	681.19	PENCE
27	290	1501.96	GUTFELD	77	664	674.5	country
28	244	1498.61	COVID	78	211	649.51	centers
29	2354	1491.2	said	79	114	647.32	SCHIFF
30	1023	1474.3	u	80	142	636.76	epidemic
31	250	1458.16	SARS	81	26375	627.84	the
32	305	1264.01	BIDEN	82	261	626.82	JAPAN
33	256	1258.14	HONG	83	101	620.3	PERINO
34	238	1240.31	mainland	84	525	606.19	united
35	329	1226.3	symptoms	85	228	600.42	airport
36	277	1221.59	CAVUTO	86	318	598.97	hospital
37	252	1211.33	KONG	87	118	594.53	INGRAHAM
38	372	1205.53	clip	88	242	589.22	announced
39	270	1153.46	cruise	89	245	588.01	novel
40	228	1105.23	BERNIE	90	269	585.74	Tuesday
41	376	1097.71	ship	91	100	580.62	BUTTIGIEG
42	182	1079.54	quarantined	92	165	579.43	FEB
43	229	1076.63	BAIER	93	97	576.35	KLOBUCHAR
44	258	1069.86	BLOOMBERG	94	136	575.8	vaccine
45	280	1056.24	deaths	95	254	570.14	Monday
46	185	1026.06	sickened	96	218	560.89	authorities
47	210	1009.43	didn	97	157	558.46	BEIJING
48	322	999.87	Wednesday	98	102	555.34	evacuees
49	203	993.76	masks	99	149	549.24	spreading
50	233	991.96	SANDERS	100	356	542.45	department

, the keyword list shows words that have specific usages from the corpora of COVID-19 news reports, such as coronavirus, virus, outbreak, infected, and CDC (Centers for Disease Control and Prevention), which are closer to the discipline of the target corpora than words on the wordlist (also see Table 2). The corpus software, based on its algorithm, retrieves 1346 keywords; however, function words, meaningless words, or simple letters such as don, u, didn, doesn, has, the, etc. still exist on the keyword list. In order to enhance the efficiency of corpus analysis, the following procedures are dedicated to optimizing the resulting wordlist and the keyword list.

Step 5. Establishing a function word list

Based on English linguistic patterns and rules [56], the researchers categorize function words into fifteen categories, which include auxiliary verbs, conjunctions, determiners (articles, demonstratives, possessive pronouns, and quantifiers), modals, prepositions, pronouns, qualifiers, question words, comparatives, conditionals, concessive clauses, frequencies, other words, negatives, and meaningless words; the total quantity of function words and meaningless characters is 228 tokens (see

Table 4. A function wordlist compilation.

Taxonomy	Tokens
Auxiliary verbs	am, are, be, being, been, do, does, did, get, got, has, had, have, is, was, were
Conjunctions	after, and, as, although, because, before, but, for, however, nevertheless, neither, or, since, so, until, when, while, yet
Determiners
Articles	a, an, the
Demonstratives	that, these, this, those
Possessive pronouns	each, hers, his, its, my, our, ours, other, others, their, whose, which, your
Quantifiers	all, any, a lot of, a few, a little, both, enough, many, most, more, much, none, several, some
Modals	can, could, may, might, must, shall, should, will, would
Prepositions	about, above, across, against, along, alongside, among, around, at, before, behind, below, beneath, beside, between, beyond, by, down, for, from, in, inside, into, near, off, of, on, opposite, out, outside, over, past, round, through, throughout, to, toward, towards, under, underneath, up, with, within, without
Pronouns	anyone, anybody, everybody, everyone, he, her, herself, him, himself I, it, me, myself, us, self, she, somebody, someone, such, they, them, you, yours, we
Qualifiers	pretty (much), quite, rather, really, somewhat, too, very
Question words	how, what, when, where, who, why
Comparatives	even, less, more, much, than
Conditionals	if, then, unless
Concessive clause	even if, even though
Frequency	ever, now
Other words	also, back, backward, backwards, forward, forwards, just, last, least, only, re, still, there, well
Negatives	couldn, don, doesn, didn, isn, no, not, non, weren
Meaningless words	b, c, d, e, f, g, h, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, ve, ll, th

). Those 228 tokens are considered the most common words that construct sentences and have the highest occurrence in articles written in English. Moreover, those tokens commonly block the efficiency of corpora analysis of specialized cases.

Step 6. Limiting wordlist range

In this step, the function wordlist compiled in step 5 is transformed into .txt format (UTF-8) and is input into AntConc 3.5.8 [30] to limit the wordlist range. After constraining the wordlist range, the keyness calculation (i.e., likelihood test) results will also be changed. Thus, the resulting wordlist and keyword list are optimized during this step.

Step 7. Generating a refined word list from the target corpora

The refined wordlist from the corpora of COVID-19 news reports is downsized to 16,325 word types (original wordlist: 16,536 word types). The eliminated 211 words were function words, meaningless words, or letters. Words that are more meaningful and more specific to the corpora emerge in the top 100 of the refined wordlist (see

Table 5. An example of the refined word list from the input corpora (partial data).

Rank	Freq.	Tokens	Rank	Freq.	Tokens	Rank	Freq.	Tokens
1	2425	coronavirus	35	525	united	69	339	three
2	2354	said	36	523	spread	70	338	number
3	2225	CHINA	37	515	public	71	332	make
4	1936	people	38	502	CDC	72	329	days
5	1585	virus	39	486	case	73	329	symptoms
6	1319	president	40	484	world	74	328	media
7	1292	health	41	466	say	75	328	south
8	1198	TRUMP	42	453	told	76	327	including
9	1184	new	43	445	CARLSON	77	327	quarantine
10	1182	cases	44	436	states	78	322	Wednesday
11	1114	one	45	424	American	79	320	February
12	1076	outbreak	46	422	government	80	319	report
13	969	news	47	416	video	81	319	take
14	967	think	48	415	flights	82	318	hospital
15	845	like	49	415	week	83	310	countries
16	840	know	50	412	house	84	305	BIDEN
17	796	going	51	395	go	85	297	press
18	724	officials	52	393	year	86	296	national
19	674	right	53	390	passengers	87	296	patients
20	669	WUHAN	54	379	city	88	294	Americans
21	666	reported	55	378	yes	89	293	international
22	664	country	56	377	day	90	293	saying
23	664	first	57	376	ship	91	290	GUTFELD
24	656	FOX	58	372	clip	92	287	years
25	620	two	59	372	medical	93	286	home
26	613	CHINESE	60	370	good	94	284	made
27	609	time	61	364	see	95	283	need
28	584	here	62	358	control	96	280	deaths
29	577	confirmed	63	356	department	97	280	tonight
30	573	travel	64	351	democrats	98	277	CAVUTO
31	566	infected	65	351	way	99	277	person
32	561	according	66	349	end	100	277	today
33	543	disease	67	349	want
34	543	state	68	345	impeachment

). This allows researchers to further expose the domain knowledge of the corpora of COVID-19 news reports.

Step 8. Generating a refined keyword list from the target corpora

After going through the optimization process, the corpus software recalculated the keyness of each word and finally extracted 2149 keywords (original keyword list: 1346 keywords). Without the interference of function words and meaningless words, keywords retrieval and analysis embraces more keywords that are closer to the disciplinary purposes and usages of the target corpora (see

Table 6. An example of the refined keyword list from the target corpora (partial data).

Rank	Freq.	Keyness	Keywords	Rank	Freq.	Keyness	Keywords
1	2425	17,780.87	coronavirus	51	185	1264.35	sickened
2	2225	12,348.46	CHINA	52	203	1253.67	masks
3	1585	10,753.89	virus	53	218	1241.01	flu
4	1076	7620.99	outbreak	54	351	1213.73	democrats
5	1198	5856.95	TRUMP	55	191	1195.39	BOLTON
6	1182	5553.66	cases	56	525	1159.65	united
7	669	4947.36	WUHAN	57	156	1159.64	HUBEI
8	1292	4617.01	health	58	416	1150.17	video
9	2354	3687.51	said	59	245	1146.14	illness
10	1319	3686.49	president	60	259	1122.74	tested
11	566	3595.57	infected	61	372	1111.22	medical
12	502	3491.79	CDC	62	206	1104.42	amid
13	656	3259.14	FOX	63	185	1081.51	respiratory
14	577	3240.89	confirmed	64	220	1072.71	airlines
15	969	3072.41	news	65	221	1063.01	prevention
16	724	2913.26	officials	66	268	1027.51	Thursday
17	445	2783.92	CARLSON	67	189	1018.97	province
18	1936	2672.81	people	68	208	997.57	KOREA
19	573	2588.52	travel	69	318	964.88	hospital
20	666	2556.98	reported	70	515	952.75	public
21	613	2543.76	CHINESE	71	181	952	princess
22	415	2468.07	flights	72	135	948.08	epicenter
23	523	2413.47	spread	73	261	938.27	JAPAN
24	327	2348.55	quarantine	74	356	937.64	department
25	543	2312.9	disease	75	193	929.77	witnesses
26	345	2209.53	impeachment	76	180	914.48	reportedly
27	390	2098.88	passengers	77	211	909.23	centers
28	290	1874.42	GUTFELD	78	269	902.27	Tuesday
29	244	1813.89	COVID	79	245	880.33	novel
30	250	1780.79	SARS	80	242	878.53	announced
31	372	1666.13	clip	81	228	875.85	airport
32	305	1650.23	BIDEN	82	254	870.34	Monday
33	329	1639.26	symptoms	83	144	865.17	PENCE
34	561	1605.28	according	84	486	856.75	case
35	256	1586	HONG	85	123	843.83	pandemic
36	277	1573.95	CAVUTO	86	296	838.98	patients
37	376	1557.85	ship	87	218	823.44	authorities
38	238	1546.05	mainland	88	142	817.56	epidemic
39	252	1533.66	KONG	89	310	811.58	countries
40	270	1496.12	cruise	90	114	794.27	SCHIFF
41	280	1408.04	deaths	91	165	785.33	FEB
42	228	1397	BERNIE	92	328	771.61	south
43	322	1396.68	Wednesday	93	328	757.84	media
44	258	1396.56	BLOOMBERG	94	157	754.61	BEIJING
45	229	1369.13	BAIER	95	101	750.77	PERINO
46	664	1357.01	country	96	274	750.18	response
47	320	1325.76	February	97	136	748.25	vaccine
48	182	1314.51	quarantined	98	118	745.83	INGRAHAM
49	1184	1313.17	new	99	149	736.02	spreading
50	233	1287.58	SANDERS	100	280	723.38	tonight

). As the quantity of keywords increase, the chances of information distortion will decrease.

Step 9. Providing critical information and reference data for decision-makers

The optimized wordlist and keyword list from the COVID-19 news corpora provide decision-makers with critical reference data to make further decisions. The optimized results of steps 7 and 8 supply decision-makers with domain-oriented words, and they can utilize those words for extracting critical information from the corpus software. Once decision-makers have sufficient information, the decisions they make will not be distorted and cause significant errors.

4.3. 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. A comparison of research methods in ESP cases.

Methods	Function Words Elimination	Machine Eliminates Function Words
Li’s approach [29]	No	No
Todd’s approach [30]	Yes	No
Ross & River’s approach [31]	Yes	No
The proposed method	Yes	Yes

). Firstly, the authors compare the elimination of function words. Li’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 Li’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, Todd’s approach [32], and Ross and River’s approach [33] focused on analyzing specific cases of COVID-19 news reports, engineering fields, and President Trump’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. Todd’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. Todd’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 Todd’s approach [30], it would save two steps in the manual five-stage filtering process and enhance the efficiency of the wordlist analysis. Ross and River’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. Ross and River’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 Ross and River’s approach [33], it would enhance the breadth of the keyword list and save the step of eliminating function words manually. That is, Ross and River’s approach [33] would no longer need to consume time in manually adjusting 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. A comparison of original and refined data of the “wordlist”.

COVID-19 News Reports Corpora			Data Types	Word Types	Tokens	TTR
Year	Month	Numbers of Reports
2020	January & February	615	Original data	16,536	457,891	3.6%
			Refined data	16,325	234,612	7%
			Data discrepancy	−211 (−1.2%)	−223,279 (−48.8%)	From 3.6% to 7%

), 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. Comparison of original and refined data of the “keyword list”.

COVID-19 News Reports Corpora			Data Types	Keyword Types	Tokens
Year	Month	Numbers of Reports
2020	January & February	615	Original data	1346	226,271
			Refined data	2149	156,841
			Data discrepancy	+803 (+89.7%)	−69,430 (−30.7%)

, the proposed approach makes corpus software recalculate the keyness 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 keyness (=17,780.87). However, before the wordlist was refined, the original wordlist showed coronavirus is ranked 24th; 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 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. Retrieved data from the top 30 clusters list.

Classifications	Clusters	Freq.	Clusters Freq./Total Freq.
Example 1	coronavirus outbreak	279	11.5%
Example 2	coronavirus in	109	4.5%
Example 3	coronavirus cases/ coronavirus case	117	4.8%
Example 4	coronavirus is	51	2.1%
Example 5	coronavirus death	27	1.1%
Example 6	coronavirus patients/ coronavirus patient	32	1.3%

). 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.

1-1

(retrieved from FOX-Jan-26-5) Coronavirus outbreak spurs Paris to cancel Lunar New Year parade…

1-2

(retrieved from FOX-Feb-4-1) A Chinese doctor who claimed he quietly warned of the coronavirus outbreak that has besieged the country…

1-3

(retrieved from FOX-Feb-15-6) …medical supplies to help China combat a coronavirus outbreak that has infected over 67,000 people.

1-4

(retrieved from FOX-Feb-17-3) …the deadly coronavirus outbreak that’s sickened over 70,500 in the country and killed at least 1,770.

1-5

(retrieved from FOX-Feb-17-4) …prayed for a cure to combat the coronavirus outbreak that has killed 1,770 people.

Example 2.

coronavirus in: this example will allow us to find where the coronavirus incidents happened (2-1, 2-5) and where the cases occurred (2-2, 2-3, 2-4).

2-1

(retrieved from FOX-Jan-20-2) Human-to-human transmission of coronavirus in China confirmed.

2-2

(retrieved from FOX-Feb-3-6) Currently, there are six cases of the novel coronavirus in California, one in Arizona…

2-3

(retrieved from FOX-Feb-10-10) Leaving someone with coronavirus in a hallway could expose countless patients…

2-4

(retrieved from FOX-Feb-11-16) Evacuee confirmed to have coronavirus in California as US total reaches 13.

2-5

(retrieved from FOX-Feb-28-1) Dog tests ‘weak positive’ for coronavirus in Hong Kong, first possible infection in pet.

Example 3.

coronavirus cases/coronavirus case: this indicates the reported medical cases that occurred in different areas, states, countries, etc. (3-1, 3-2, 3-3, 3-4, 3-5).

3-1

(retrieved from FOX-Jan-24-7) CDC confirms coronavirus case in Illinois, dozens more under investigation.

3-2

(retrieved from FOX-Jan-27-13) CDC: 110 suspected coronavirus cases in US under investigation…

3-3

(retrieved from FOX-Jan-27-11) Africa investigating first possible coronavirus case in Ivory Coast student: officials.

3-4

(retrieved from FOX-Feb-13-24) The number of coronavirus cases in China significantly increased on Thursday…

3-5

(retrieved from FOX-Feb-21-13) Coronavirus cases balloon in South Korea as outbreak spreads.

Example 4.

coronavirus is: the grammatical structure of noun + auxiliary verbs (such as am, is, are, etc.) may represent important information regarding explanations and definitions of the noun. In 4-1, it explains the transmission routes of coronavirus. In 4-2, it defines the type of coronavirus. In 4-3, it explains the origins of the coronavirus. In 4-4, it shows an expert’s point of view on the disease; 4-5 indicates President Trump’s mocking insight on the coronavirus.

4-1

(retrieved from FOX-Feb-10-1) The coronavirus is primarily transmitted through respiratory droplets, meaning human saliva and mucus.

4-2

(retrieved from FOX-Feb-11-3) The new coronavirus is a respiratory virus, and we know respiratory viruses are often seasonal, but not always.

4-3

(retrieved from FOX-Feb-24-3) The strain of coronavirus is believed to have jumped from bats and snakes…

4-4

(retrieved from FOX-Feb-26-24) Dr. Marc Siegel said Wednesday that coronavirus is appearing to be more contagious than the flu.

4-5

(retrieved from FOX-Feb-29-17) Donald Trump: Coronavirus is Democrats’ ‘new hoax’.

Example 5.

coronavirus death: this example helps us to extract information about coronavirus death cases chronologically. In 5-1, the news reports on 24 January indicated that there had been 41 deaths in China. In 5-2, the first overseas death was reported in the Philippines on 2 February. In 5-3, the second death outside of mainland China was reported in Hong Kong. In 5-4, coronavirus deaths increased to 105 on 16 February. In addition, 5-5 reported that Japan announced its first death case.

5-1

(retrieved from FOX-Jan-24-2) Coronavirus death toll rises to 41 in China, more than 1200 sickened.

5-2

(retrieved from FOX-Feb-2-5) Coronavirus death in Philippines said to be first outside China.

5-3

(retrieved from FOX-Feb-4-4) A second coronavirus death outside of China was reported earlier Tuesday by Hong Kong…

5-4

(retrieved from FOX-Feb-16-3) China sees coronavirus death toll rise by 105.

5-5

(retrieved from FOX-Feb-18-2) Japan announced its first coronavirus death last Thursday.

Example 6.

coronavirus patients/coronavirus patient: this indicates related diagnostic tests (6-1, 6-5), medical treatments (6-2), and medical cases (6-3, 6-4).

6-1

(retrieved from FOX-Jan-23-8) Suspected coronavirus patients in Scotland being tested: reports.

6-2

(retrieved from FOX-Feb-18-3) Japan to trial HIV medications on coronavirus patients.

6-3

(retrieved from FOX-Feb-18-15) ‘SARS-like damage’ seen in dead coronavirus patient in China, report says.

6-4

(retrieved from FOX-Feb-19-9) Iran’s first 2 coronavirus patients die, state media says.

6-5

(retrieved from FOX-Feb-26-20) First US docs to analyze coronavirus patients’ lungs say insight could lead to quicker diagnosis.

To conclude this section, the first part of the discussion demonstrated how the proposed approach could be imported into different researchers’ corpus-based approaches to replace their experimental steps of manually removing function words, increase the efficiency of their analysis, and save time. The second part of the discussion presented quantitative data to show the verification of the proposed approach in removing function and meaningless words significantly, increasing the efficiency of the corpus analysis in an ESP case. In the final part of the discussion, IR from the big textual data, also called knowledge extraction, was illustrated to shed light on the value of the proposed optimized corpus-based approach to ESP big textual analysis.

5. Conclusions

During the advanced ICT era, efficient analysis, integration, comparison, and synchronized calculation of big data is one of the important sources of momentum to stimulate industrial progress. Moreover, big data embraces not only digits but also big textual data. In Industry 4.0 research fields, one of the important development trends is making AI understand NLs to generate more intimate human–computer interactions, and implement big data analysis in order to improve the efficiency of production and information processing. Furthermore, from the perspective of social sciences, big textual data is inseparable from the rising of social networks such as Facebook, Twitter, online news reports, etc. People spread information or communicate via digital self-media daily; and the resulting data is recorded on the internet cloud that can be used to analyze human behavior patterns, habits, communication methods, and so on to further facilitate industrial developments. Recently, a novel coronavirus, COVID-19, has raged through almost every country in the world. COVID-19, its notorious medical term, has become the most popular and most frequently-searched word on several search engines and media website; the disease has caused great panic for people and damaged national systems and global economics. To alert people about COVID-19, abundant medical information and news reports related to COVID-19 have been written in English and spread rapidly in cyberspace. That is, big textual data has become one of the main formats of COVID-19 big data.

Recently in many ESP research cases, corpus-based approaches for extracting domain-oriented and technical words are popularly adopted by researchers. However, the authors found that many researchers still use manual annotations to remove function words and meaningless words, which decrease the efficiency of corpus analysis and can easily cause bias. In order to improve the efficiency of corpus analysis in COVID-19 big textual data, in this paper, the researchers propose a novel machine-optimizing corpus-based approach to eliminate function words and meaningless words to enable users to more closely and quickly contact domain-oriented words from COVID-19 news reports.

The proposed approach presents significant optimizing results for corpus analysis in processing the big textual data of COVID-19. The notable contributions of the proposed approach can be summarized as follows: (1) for function words elimination and machine elimination of function words, the proposed approach uses the machine mechanism to perform optimization tasks and is more efficient than listing three approaches; (2) for data discrepancy, it indicates the reliability and validity of the proposed approach quantitatively; (3) for demonstration of knowledge extraction, the proposed approach also reveals the advantages of retrieving domain-oriented words via corpus software without interference by function words and meaningless words.

In the future, the proposed approach can be widely adopted to optimize corpus analysis results or to enhance the efficiency of corpus-based approaches, especially in cases of extracting domain-oriented lexical units. This paper’s research results from linguistic angles would provide valuable English linguistic patterns that can be utilized in ICT fields of machine learning, machine translation, deep learning, NLP, AI, and more. In addition, techniques used in streaming data are also important future development indicators to effectively and rapidly intercept the latest data (especially in the case of COVID-19) to timely expand and update a big textual database. It will allow corpus-based approaches in big data analysis accompanied by more accurate, efficient, and up-to-date analytical results.