## 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, [PERSON] 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, [PERSON] 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, [PERSON] 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. [PERSON] 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. [PERSON] [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. [PERSON] [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.