## 2 Materials and Methods
### Research Method
#### 2.2.2 Using Topic Modeling as a Semantic Technology to Examine Novelty and Contribution Claims  
The analysis of the content of the research articles was based on performing a topic modeling analysis [19; 20; 21] on the corpus of 104 extracted paragraphs that were identified  
Figure 2: A visual representation of the first seven novelty contribution claims within the Excel file containing the corpus of all 104 claims. Each claim was labeled in a way that could indicate its location in the original article.  
as novelty claims and research contributions. Each claim paragraph was considered an individual text document. We have used the text analytics capabilities of Orange Data Mining software (version 1.31.1, [[https://orangedatamining.com/](https://orangedatamining.com/)]([https://orangedatamining.com/](https://orangedatamining.com/))) and adopted the most popular topic modeling algorithm, LDA [19] (Figure 3). The advantages of using the Orange Data Mining tool are that it is a well-documented, well-maintained, and reliable open-source tool.  
Figure 3 illustrates the logic of the topic modeling process. The uploaded corpus of text documents is pre-processed by the Preprocess Text widget, which splits each text into smaller units (tokens), filters them, runs normalization such as stemming and lemmatization, and removes stopwords. These are common, frequently occurring words that are removed from the text before the analysis because they are considered to have little semantic meaning and can obscure the underlying topics within a document. Removing such stopwords is an important (and sometimes time-consuming) step that allows the model to focus on more informative words that help identify more distinctive topics.  
The LDA algorithm (selected in the Topic Modeling widget) considers every pre-processed text document (novelty/contribution claim) as a mixture of topics and every topic as a mixture of words. Words can be shared between topics, and the topics can be shared among documents. The LDA algorithm identifies combinations of words that are semantically interrelated and tend to appear together across different documents. The combinations of words help the identification of the specific themes that are latently present in the corpus. In addition, LDA organizes the corpus by clustering the documents corresponding to each topic. The claim paragraphs clustered in each topic are ranked in terms of the degree of their association with it. A closer examination of the topical organization of the paragraphs enables the interpretation of the overall theme and the labeling of the topics [21].  
Figure 4 shows a snapshot of the Data Table shown in Figure 3. The first three columns are the same as the ones shown in Figure 2. The last five columns (Topic 1, Topic 2, \(\ldots\), Topic 5) show the degree of association of each claim on the left with each of the five topics. The claims are ranked in terms of the degree of their association with Topic 1. Selecting the claims that have a degree of association higher than 0.6 provides the list of claims that are used for the interpretation of Topic 1. The analysis follows the same logic to identify the claims that are most highly associated with the rest of the topics. The research design provides an example of an actionable semantic technology framework enabling the automation of the categorization and processing of textual data to uncover emerging latent themes and enhance its overall comprehensibility.  
Figure 3: Schematics of the Orange Data Mining workflow used to perform the topic modeling analysis.