Hossein Amirkhani

Papers

Total Citations

104

H-Index

About

Hossein Amirkhani is a leading researcher in bibliometrics and natural language processing, whose work has significantly shaped the understanding of emerging trends in artificial intelligence. His research primarily focuses on the quantitative analysis of scientific literature, with key contributions to the study of sentiment analysis and graph neural networks (GNNs). Amirkhani’s most cited work, "Bibliometrics of sentiment analysis literature" (2018, 88 citations), provides a comprehensive evaluation of research trends in sentiment analysis using Web of Science data, offering critical insights into the evolution of this field. He further advanced the field with "Graph Neural Networks: a bibliometrics overview" (2022, 16 citations), a pioneering Scopus-based study that tracks the trajectory of GNN research since its inception in 2004. Through these contributions, Amirkhani has established himself as a key figure in bibliometric analysis, helping researchers and students understand the landscape of cutting-edge AI topics. His work is essential for anyone seeking to grasp the quantitative dynamics of modern machine learning research.