Co-word Analysis of the Persian Translation of Nahj al-Balagheh Letters Using Scientometric Approach: Identifying Key Concepts and Semantic Network through Centrality Measures and Clustering

Abstract
Purpose: Co-word analysis has been employed to map the structure and relationships among topics, concepts, and terms present in the studies of the Persian translation letters of Nahj al-Balagheh. Scientometric approach: identifying key concepts and semantic networks by centerlity measures and clustering were used. These techniques help identify hidden patterns in the text and provide a deeper understanding of the structure and content of these letters.
Methodology: The research conducted is descriptive-analytical, utilizing an inductive and exploratory approach through document analysis. Its objectives are aimed at practical applications in science policymaking, employing content analysis techniques. The qualitative data and statistical population for this study consist of the Persian translation of the letters from "Nahj al-Balagheh" by Imam Ali (PBUH), where 79 letters were uploaded in a Word file based on the research goals. Key vocabulary was extracted through indexing and saved in Excel format. For topic mapping and clustering, important vocabulary lists were selected based on Bradford's law and keyword inclusivity. The PreMap was used for extracting terms and creating the matrix. SPSS was employed for clustering, while UCInt and NetDraw were utilized for clustering analysis and topic visualization. 79 Nahj al-Balagheh letters from the translation by Mohammad Dashti were compiled. Each letter was studied to select meaningful keywords, which were then standardized and stored in a Word file. After standardizing the keywords, a threshold was set for preparing the co-occurrence matrix. The PreMap facilitated the creation of a co-word matrix, resulting in a square matrix that indicates how frequently each keyword co-appears with others in the documents. The dimensions of the matrix correspond to the number of selected concepts, with each entry showing the frequency of co-occurrence between pairs of keywords. For the current study, a 79x79 matrix was created by selecting 79 frequently occurring keywords. Hierarchical clustering was employed for co-word analysis, leveraging its ability to delineate clusters relevant to each keyword and illustrate the relationships among them. Using SPSS, hierarchical clustering was conducted with the Ward method, and a dendrogram of co-occurring terms was generated. The resulting matrix was transformed into the desired format and called in the UCInt software to obtain degree centrality, closeness, and betweenness indices. Subsequently, to visualize the degree centrality, closeness, and betweenness maps, the NetDraw extension of UCInt was utilized.
Findings: Results indicate that the keyword “people” ranks first in terms of word frequency with 26 occurrences. It aligns with Quranic verses highlighting the importance of the mutual relationship between the ruler and the people. Thus, the reciprocal relationship between the ruler and the people is one of the most important social relationships, and understanding the mutual duties and obligations is essential. The key findings reveal that the highest ranks in terms of degree centrality, betweenness centrality, and closeness centrality belong to the words “world,” “people,” and “truth,” respectively. Degree centrality is calculated based on the number of incoming or outgoing connections of each node. The basis of all events occurring for people is the “world,” as it can play both positive and negative roles according to Imam Ali’s teachings. The world can either be a source of growth and development or cause blindness and lead to misguidedness. If one views the world with insight, it offers lessons and guidance, serving as a means of salvation and spiritual and moral elevation. Conversely, the world can blind people and obscure their truth-seeing vision, leading to destruction. The word “people” has the highest rank in terms of betweenness centrality. In line with this finding, the words of Imam Ali regarding efforts to strengthen societal bonds also support this result. He calls Muslims brothers and urges them to unite to combat discord. In another sermon, Imam Ali states, "God's hand is with the community; avoid division and separation, for whoever leaves the community falls prey to Satan, just as a stray sheep becomes the prey of a wolf”. Based on the closeness centrality metric, nodes with high closeness centrality are not only more accessible to other nodes but also wield greater influence within the network, thereby playing the most central role. According to a study, the keyword “truth” has the highest rank in terms of closeness centrality.
Conclusion: From the perspective of Nahj al-Balagheh, “truth” is a word capable of bearing numerous interpretations and descriptions, as Imam Ali referred to it as “the most comprehensive thing in description,” saying: “Indeed, the truth is the most spacious thing in description”. It indicates that the word “people” has the highest frequency. Accordingly, it is essential for politicians and government officials, like Imam Ali, to consider the welfare of the people as a primary concern and to prioritize gaining the people’s satisfaction in governance.

Keywords: Nahj-al-Balagheh, Imam Ali, Persian translation of Nahj-al-Balagheh letters, Co-word analysis, Scientometrics, Centerality measures and clustering