A TEXT-MINING-BASED SCIENTOMETRIC ANALYSIS OF SUNSPOT NUMBER PREDICTION

The activity of the Sun is a substantial driver of both the terrestrial and space environments, making the study and prediction of solar activity and its cycles crucial. Predicting the sunspot number index (SN) is particularly important; this parameter, referring to the number of sunspots and sunspot groups on the Sun’s photosphere, is a critical indicator of solar activity. With solar storms adversely affecting power grids, satellite operations, and communication systems, the ability to predict SN with reasonable accuracy is exceptionally helpful. As a consequence, there has been growing academic interest in forecasting SN and its behavior, with a variety of methodologies being applied to the problem. However, the rapid increase in the number of publications is making it difficult to have a clear overview regarding the most novel or prolific topics, as well as the most prominent authors or countries in the field. In this work, we use text mining to conduct a scientometric analysis of extant scientific literature on sunspot number prediction since 1927. Using VOSviewer software and Scopus data, we elucidate how the literature in this research field has evolved, showing the publications in terms of their country of origin (including co-authorship), source of publication, most relevant topics, and most cited elements based on journal and author. Our findings show that sunspot prediction (especially regarding SN) is an established field gaining renewed interest due to its important contribution to our knowledge of solar activity.