Analyzing the United Nations Speeches with a Neutrosophic Approach to Text Mining in The Context of Türkiye’s Foreign Policy

Abstract

Document clustering is an integral and important part of text mining. In case of classical clustering, data item belongs to only one cluster, whereas in Plithogenic approach to fuzzy clustering, data point may fall into more than one cluster. Thus, Plithogenic approach fuzzy clustering leads to wherein each data point is associated with more than one membership function that expresses the degree to which individual data points belong to the cluster. Additionally, his speeches at the UN will be analyzed with a neutrosphic approach. With the help of this approaches, in this study, speeches of the UN sessions attended by Turkish diplomats in the United Nations (UN) will be analyzed to understand the priorities of the country in international politics, the change and continuity in these policies. The texts of the UN sessions attended by Turkish diplomats between 2015 and 2023 were taken as data set. Such a large volume of data was analyzed with the help of text mining. For this purpose, each year's data was analyzed separately using word frequencies and clustering analysis, and then topic modeling was performed for each year's data using the Latent Dirichlet Allocation (LDA) method.