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 studies of the Persian translation letters of Nahj al-Balagheh. A scientometric approach, including the identification of key concepts and semantic networks through centrality measures and clustering, was used. These techniques help identify hidden patterns within the text and provide a deeper understanding of the structure and content of the letters.Methodology: This research employs a descriptive-analytical design, utilizing an inductive and exploratory approach through document analysis. The study aims to support practical applications in science policymaking by employing content analysis techniques. The qualitative data and statistical population for this study consist of the Persian translation of the letters from "Nahj al-Balagha" by Imam Ali (PBUH). A total of 79 letters were compiled into a Word document according to the research objectives. 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 tool was used to extract terms and create the matrix. SPSS was employed for clustering, while UCINET and NetDraw were utilized for cluster analysis and topic visualization. A total of 79 letters from Nahj al-Balagha, translated by Mohammad Dashti, were compiled. Each letter was examined to select meaningful keywords, which were then standardized and stored in a Word document. After standardizing the keywords, a threshold was established to prepare the co-occurrence matrix. PreMap facilitated the creation of a co-word matrix, resulting in a square matrix that indicates how frequently each keyword co-occurs with others in the documents. The matrix dimensions correspond to the number of selected concepts, with each entry representing the frequency of co-occurrence between pairs of keywords. For this study, a 79 × 79 matrix was created by selecting 79 frequently occurring keywords. Hierarchical clustering was employed for co-word analysis, leveraging its ability to identify clusters relevant to each keyword and to illustrate the relationships among them. Using SPSS, hierarchical clustering was performed with the Ward method, and a dendrogram of co-occurring terms was generated. The resulting matrix was transformed into the desired format and imported into the UCINET software to calculate degree centrality, closeness, and betweenness indices. Subsequently, the NetDraw extension of UCINET was used to visualize the maps of degree centrality, closeness, and betweenness.Findings: Results indicate that the keyword "people" ranks first in word frequency, appearing 26 times. This aligns with Quranic verses emphasizing the significance of the mutual relationship between the ruler and the people. Therefore, the reciprocal relationship between the ruler and the people is one of the most important social bonds, and understanding their mutual duties and obligations is essential. The key findings reveal that the highest ranks in 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 and outgoing connections of each node. According to Imam Ali’s teachings, the “world” is the foundation of all events affecting people; it can play both positive and negative roles. 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 ability to see the truth, leading to destruction. The word “people” holds the highest rank in terms of betweenness centrality. This finding is further supported by the words of Imam Ali regarding efforts to strengthen societal bonds. He refers to Muslims as brothers and urges them to unite in order 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" ranks highest in terms of closeness centrality.Conclusion: From the perspective of Nahj al-Balagha, the term "truth" encompasses numerous interpretations and descriptions. Imam Ali referred to it as "the most comprehensive thing in description," stating, "Indeed, the truth is the most spacious thing in description." This highlights that the word "people" appears with the highest frequency. Accordingly, it is essential for politicians and government officials, like Imam Ali, to prioritize the welfare of the people and to seek their satisfaction as a primary concern in governance.