# State of Industry 5.0--Analysis and Identification of Current Research Trends
## 2 Data Gathering and Preprocessing  
The data gathering process involved identifying and extracting data of published research articles from scholarly databases. The databases used to extract data for this study include IEEE (Institute of Electrical and Electronic Engineers), Science Direct, and MDPI. These databases have a wider variety of coverage in terms of sources than other databases which is important since publications focusing on Industry 5.0 may be outside the \"top\" journals in the field [9]. Further, the restricted access to the authors of the databases available is another factor in choosing the identified sources and restricting the analysis to the abstracts. To identify published articles addressing the topic of Industry 5.0, key term \"Industry 5.0\" was used to search the metadata and identify published articles.  
This included any published articles mentioning \"Industry 5.0\" in the title, abstract, or the keywords. This is because Industry 5.0 is still an emerging term, and it is still unclear what other key terms and synonyms are used [9]. The keyword search produced a total of 196 documents which included 26 from IEEE, 76 from MDPI, and 94 from Science Direct. The time range for these documents is 2016-2022 indicating the earliest publication on Industry 5.0 in 2016. The data collected included the publisher, title, publication year, and abstract for each publication retrieved. The data was then sorted by the database it was retrieved from and converted for further analysis into an.xlsx file. Once sorted, the data was labeled to identify the publisher, title, and the abstract corresponding to each published article retrieved. To analyze the gathered data, \"R\" a statistical language widely used by statisticians and data miners was utilized to transform, visualize, and analyze the data. This included data preprocessing, transformation, key term extraction, frequency analysis, and topic modeling. Table 2 illustrates the search results of each individual data base the abstracts were extracted from.  
The data is converted into a. csv format to be imported to R and then cleaned for further analysis by removing unwanted characters such as white spaces, numbers, symbols, and tag from the abstracts. Once these characters are removed the next steps were to delete stop words and convert words to lower case. Several text mining tasks, facilitated by Quanteda package [10] and dpyIr package in R, include removing common words from documents. Stop word removal is a process of removing commonly occurring words for conjunction and propositions such as, _a, I, in, for, with, the, not, on,_ and several similar other words that do not usually contribute much to the meaning of a given sentence. Most text written in English language follows punctuation and use of lower case and upper-case text. Though capitalizations enable humans to differentiate between nouns and proper nouns, in text analysis, words irrespective of where they are capitalized are treated equally  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **Abstracts** & **Timeline** \\ \hline IEEE Explore & 26 & 2019–2022 \\ Science Direct & 94 & 2016–2021 \\ MDPI & 76 & 2018–2021 \\ \hline Total & 196 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key Term ‘Industry 5.0’ Search Results.  
and thereby converting all the characters to either lower case or upper case. There is also the process of stemming and creating vectors. Stemming helps to standardize the text by prefixes, suffixes, and inappropriate pluralization's in the text document. Creating vectors involves transforming the data into a representation to act as a suitable input for text mining algorithms.