## 2 Materials and Methods
### Extracting the Influencing Factors Using Text Mining
#### 2.4.1 Text Mining for Influencing Factor Extraction  
Text mining aims to derive useful information or knowledge from unstructured and vast textual data [50; 51; 52]. The authors of [50; 51] reviewed text mining tools used in literature review processes, such as search strategies, screening citations, abstracting/summarizing data, and classifying text. Cost-effectiveness analysis showed that citation screening with text mining reduced more than 60% of the title-abstract screening workload compared with other screening approaches without text mining [53]. Text mining has also been carried out together with the PRISMA method, such as in [54; 55], where it was applied for full-text screening of injury and energy consumption research, respectively. In our study, however, text mining was applied to complement the research by finding new influencing factors from other domains outside NPP by using a topic modeling algorithm, which is a method used to discover the topics of documents. We applied latent Dirichlet allocation (LDA) for topic modeling, which is a valid, highly effective unsupervised learning method that has been used in various studies (smart service systems, electronic petitions, social media strategy, wearable technology, etc.) [56; 57; 52; 58]. We carried out the following: data collection, data preparation, topic modeling using LDA, and results analysis (extracting the influencing factors). The data are the title, abstracts, and author keywords of the papers collected using the same search words used for PRISMA. The papers were from the Scopus database, and we included all the papers, not only the NPP domain papers. Data preparation consisted of removing stopwords (insignificant words, from an existing library and manually added from pilot tests), removing words with fewer than 3 characters, and lemmatization (transforming a word into its dictionary \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Transformation**} & \multicolumn{2}{c}{**Example**} \\ \cline{2-3}  & **(Transformed) Influencing Factor** & **Finding’s Title on Source Paper** \\  & & **[Source Paper]** \\ \hline Minor rename & Clarity in role/responsibility & Clarity of team members’ roles [33] \\ \hline Rename for clarity & Levels of information processing before speech & A simple model for the differences in statement levels [13] \\ \hline Rename for generality & Team size and team members & Operations team of a typical U.K. nuclear power station [47] \\ \hline Specification of the content—the same sub-element & Working memory of colleagues’ goals, priorities, and activities. & Relating cognitive processes to overt behavior. Team-specific: Information distribution, task allocation, and management [48]. \\  & Evaluation of team value of information and knowledge. & \\  & Periodic newscasting of own goals, priorities, and activities. & \\ \hline  & Sub-element: Cognitive functions. & \\  & Influencing factors: Level of stress. & \\  & Sub-element: Team situation awareness. & \\  & Influencing factors: Team situation awareness model & Causal conceptual model of team situation awareness [9] \\  & development and assessment. & \\  & Sub-element: External. & \\  & Influencing factors: Workspace comfort, safety culture, quality of digital procedures, quality of interfaces. & \\ \hline Combine identical concepts & Clarity in role/responsibility & Clarity of team members’ roles [33]. \\  & Team elements: Role/responsibility [49]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The transformation from findings to influencing factors. form). We calculated the term frequency-inverse document frequency, which represents the importance of a word in a paper and all the data, and then compressed the data. Using the prepared data, a document-word matrix was created, and LDA was applied using the library from scikit-learn and pyLDAvis [59; 60]. In LDA, a document may contain multiple topics, and each topic is represented by a collection of words [57; 61].