## 4 Experiment and Results
### Dataset
#### 4.1.2 Analyzing Publication Records  
This section discusses data pre-processing and the analysis of the publication from two perspectives: the temporal evolution of authors' publications and keywords analysis. In text mining research, data pre-processing is a common task for enhancing the efficiency of the final results. In this research, the main objective of this process is to obtain the required dataset, including the author's publication specifications; hence, the processing strategy may differ depending on the type of data. Data cleaning was the first step to be carried out and included lowercase and punctuation removal as well as the removal of duplicates and papers that did not originally include the relevant keyword phrases (e.g., book chapter). Data lemmatization and tokenization were then conducted, including stop word removal. As a result, the final data set contained a total of 3628 publication records.  
Temporal analysis: This is one of the most effective strategies for analysis, since it helps to examine large amounts of data and to highlight their similarities and differences. Therefore, analyzing the authors' publications leads to an understanding of their significance and influence on scholarly communities. The data was examined from different perspectives, such as the number of publications, the continence range of publishing years, and the significance of the citation. As shown in Figure 10, the distribution of the publication years is scattered, which indicates that some of the authors started publishing much earlier than others. Additionally, the data shows that there were significant differences in the citation importance and number of publications in the same year; the highest number of publications was in 2020, whereas the publications in 2013 had the highest citation count, even though there was a lower overall number of publications over the course of that year. However, it is worth noting that a 2020 publication has not been published for a long period of time and is therefore likely to have a lower volume of citations. At the same time, this statistic indicates the significance of the papers that were published in 2013 and their distinguished content.  
Figure 11 shows the total number of publications for each author in the data set, which can be used to assess the statistical significance. [PERSON] has the highest number of papers, while [PERSON] has the lowest number. Along with that, Figure 12 illustrates the publication and citation distribution over the years for both authors, and it can be seen that even though [PERSON] has a low number of publications, the number of citations was relatively high. Therefore, the number of publications alone does not reflect the significance of a publication. Furthermore, the number of citations cannot be considered in isolation. Instead, all three factors (year distributions, publications number, citations number) should be considered equally important in the exploration of the significance of a publication.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute Name** & **Attribute Description** \\ \hline Cites & Number of citations per paper \\ Authors & Authors’ initial first name, last name \\ Title & Title of the paper \\ Year & Paper publication year \\ Keywords & Paper keywords \\ Affiliations & The academic institute that the author belongs to \\ \hline \hline \end{tabular}
\end{table}
Table 2: The key attributes of the used dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Publications** & **Universities** & **Years Range** & **Citations** \\ \hline
120 & 3898 & 12 & 43 & 39,363 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of the collected data.  
Figure 12: Two authors’ publication and citation distributions over the years.  
Figure 13: Publications’ statistical analysis of year ranges.