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

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.

A Bibliometric Analysis of Text Mining: Exploring the Use of Natural Language Processing in Social Media Research

“…Although it started from simple approaches such as morphological analysis, nowadays, with the help of techniques based on machine learning, it manages to learn complex models, perform sentiment analysis, translate text automatically, provide virtual assistance, build chatbots, extract data, provide summaries, and perform numerous other tasks. The progress in this area has been synthesized in various studies from the field, such as those by Ortiz-Garces et al [7], Chang [8], Hirschberg [9], Zhang et al [10], Jiang et al [11], and more.…”