Positioning Natural Language Processing Use for Emotion Detection from a Humanistic Perspective: A Bibliometric Study

The burgeoning use of Natural Language Processing (NLP) across diverse disciplines has significantly broadened the scope of this analysis technique. Integrating NLP into complex domains such as emotion detection (ED) brings forth the potential of NLP beyond traditional realms of sciences, extending its influence into humanistic fields like arts, humanities, and social sciences. Although several bibliometric studies have explored various implications of NLP, no significant work has focused specifically on trends in NLP within the context of ED. Thus, this study aims to investigate the global evolution and publishing trends of the intersections of NLP and ED through the Scopus database. Employing visualization tools such as RStudio and VOSviewer, this comprehensive analysis of 984 research articles, reveals publication trends from 2000 to 2023. The findings of the paper indicate the primary subject areas and leading countries with scholarly contributions. The findings also identified key themes that provide the scope for future researchers to understand the main concepts that define NLP and ED. Thus, helping readers and future scholars navigate organizing data is crucial for building strong theoretical models and practices in this rapidly growing area.