A Bibliometric Analysis of Natural Language Processing and Classification: Trends, Impact, and Future Directions - Setiawan Ardi Wijaya; Rahmad Gunawan; Rangga Alif Faresta; Asno Azzawagama Firdaus; Gabriel Diemesor; Furizal (PT. Teknologi Futuristik Indonesia, 2025)
This study presents a bibliometric analysis of Natural Language Processing (NLP) and classification research, examining trends, impacts, and future directions. NLP, a key field in artificial intelligence, focuses on enabling computers to process and understand human language through tasks such as text classification, sentiment analysis, and speech recognition. Classification plays a crucial role in organizing textual data, facilitating applications like spam detection and content recommendation. The research employs bibliometric analysis to evaluate publication trends, citation networks, and emerging themes from 1992 to 2025. Using data retrieved from Scopus, descriptive statistical analysis and bibliometric mapping with VOSviewer reveal key contributors, influential publications, and subject area distributions. Findings indicate a significant rise in NLP research, with deep learning models, particularly transformers, driving advancements in the field. The study highlights dominant research areas, including computer science, engineering, and medicine, and identifies leading countries in NLP research, such as the United States, China, and India. Additionally, ethical concerns, including bias and fairness in NLP applications, are discussed as critical challenges for future research. The insights derived from this analysis provide valuable guidance for researchers and policymakers in shaping the next phase of NLP development.