A systematic literature review on Natural Language Processing (NLP)

Abstract

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) technology used by machines to understand, analyze and interpret human languages. In the past decade, NLP received more recognition due to innovation in information and communication technology which led to various research. Thus, it is essential to understand the development taken in the knowledge of literature. The present study aims to present a systematic literature review using bibliometric analysis in NLP research. The study identifies the publication trends, influential journals, cited articles, influential authors, institutions, countries, key research areas, and research clusters in the NLP field. 12541 NLP publications were extracted from the Web of Science (WoS) database and further analyzed using bibliometric analysis. The result indicated that the first NLP publication was in 1989, with the highest publication recorded in 2021. The IEEE access journal was the leading journal with the highest number of publications, and the highest number of citations received for NLP articles is 3174. The most productive author in the NLP field is Liu HF, whereas Harward university is the most influential institution. The US is the leading country in the total number of publications. Researchers extensively researched applied sciences area. The findings further revealed that most of the NLP research focused on five main clusters: modeling, neural networks, artificial intelligence, data mining using social media platforms, and data capturing and learning.