Visualized Analysis of Intention Recognition Research Based on CiteSpace

Abstract

Intention recognition has improved the accuracy and speed of interactions between human and machine. However, due to the rapid development, the diversity of research content and methods, and the wide range of applications, researches in this field lack systematic integration. Based on 1234 selected papers, this paper applied visualization methods in CiteSpace to figure out significant country, institution, journal, reference and keywords to demonstrate the developments and research hotspots from 2011 to 2021. He Huang and Aaron J. Young are thought to be the most productive authors. According to the keywords analysis, "Hidden Markov Model", "networks", "feature extraction" are current hotspots. Furthermore, the deficiencies and future directions have been indicated by conducting this composite analysis.