Semantic Web Technologies for Digital Transformation: A Network Perspective

Abstract

Semantic Web Technologies for the Digital Transformation offer a framework for analyzing and understanding the vast and complex information generated across various digital platforms. In this study, we collected data from X (formerly Twitter), Instagram, and online news articles using the keyword “digital transformation.” Preprocessing techniques such as text cleaning, tokenization, stopword removal, and lemmatization were applied to ensure high-quality data. Feature extraction was performed using GloVe embeddings, capturing the contextual and semantic meanings of words in numerical form. Furthermore, Semantic Network Analysis was employed to identify relationships and patterns between key concepts, providing insights into the discourse on digital transformation. Through community detection and centrality analysis, the study identified thematic clusters and influential topics that shape the conversation on digital transformation.