Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic Review

Yunqing Jiang, Patrick Cheong Iao Pang, Dennis Wong, Ho Yin Kan

Research output: Contribution to journalReview articlepeer-review

2 Citations (Scopus)

Abstract

Natural language processing (NLP), which is known as an emerging technology creating considerable value in multiple areas, has recently shown its great potential in government operations and public administration applications. However, while the number of publications on NLP is increasing steadily, there is no comprehensive review for a holistic understanding of how NLP is being adopted by governments. In this regard, we present a systematic literature review on NLP applications in governments by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The review shows that the current literature comprises three levels of contribution: automation, extension, and transformation. The most-used NLP techniques reported in government-related research are sentiment analysis, machine learning, deep learning, classification, data extraction, data mining, topic modelling, opinion mining, chatbots, and question answering. Data classification, management, and decision-making are the most frequently reported reasons for using NLP. The salient research topics being discussed in the literature can be grouped into four categories: (1) governance and policy, (2) citizens and public opinion, (3) medical and healthcare, and (4) economy and environment. Future research directions should focus on (1) the potential of chatbots, (2) NLP applications in the post-pandemic era, and (3) empirical research for government work.

Original languageEnglish
Article number12346
JournalApplied Sciences (Switzerland)
Volume13
Issue number22
DOIs
Publication statusPublished - Nov 2023

Keywords

  • co-word analysis
  • government
  • literature analysis
  • natural language processing
  • network analysis
  • public administration

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