Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic Review
Abstract
:1. Introduction
2. Background
2.1. Natural Language Processing
2.2. Natural Language Processing in Governments
3. Material and Methods
3.1. Search Strategy
3.2. Screening Strategy
- The use of NLP should play a significant or major part in the study (its research objective, questions, etc.). Studies with an unrelated or secondary focus on the usage of NLP were excluded from this phase. For example, those articles may use other methods in AI but merely mention NLP technologies and do not use them in their research.
- NLP use in government (or the public sector) should be the main focus of the study, and the study’s objective should directly serve or benefit governments. For example, if NLP was not applied in the context of a government, or the authors only used the open data source from the government to conduct research in other fields, these articles were excluded.
3.3. Data Analysis
4. Results
4.1. Descriptive Results
4.2. Co-Word and Network Analysis
5. Discussion
- (1)
- Automation: NLP technologies are leveraged for automating processes and activities under this classification. Exemplar literature includes a discussion of an automatic system to offer COVID-19 information to German citizens [67];
- (2)
- Extension—NLP technologies are being utilised to support novel forms of governance that enhance rather than replace current procedures or activities, such as to enhance the algorithms for fake news identification [68];
- (3)
- Transformation—This category refers to the innovative forms of governance made possible by NLP technologies with the potential to replace or alternate the established ones. For example, chatting robots as a new form of citizen-to-government communication [69].
5.1. NLP for Governance and Policy
5.2. NLP for Understanding Citizen and Public Opinion
5.3. NLP for Medical and Healthcare
5.4. NLP for Economy and Environment
5.5. Implications for Future Research
- (1)
- The Potential of Chatbots: The literature analysis reveals a lack of interest in chatbots, though government agencies have gradually shown their interest in chatbots recently, such as [65,95]. Chatbot research is an active NLP research subfield, but the same may not be true in governmental research, given that “the development of chatbots for public administration services has received very little attention” [96]. In fact, chatbots are being used to perform a wide variety of tasks, for instance, placing orders for meals, making product recommendations, providing customer support, setting up meetings, etc. During the pandemic, chatbots’ ability to “chat” with people caught the focus of governments, and they are used as a solution for maintaining conversational engagements under social distancing policies [97]. However, there are few chatbot applications developed for local administrative services [96]. With the introduction of ChatGPT, the conversational capabilities of AI-driven tools have reached the eyesight of the public. It can carry on a conversation by picking up and comprehending human languages and engaging in dialogues in accordance with the chat’s context. One of ChatGPT’s first models can successfully talk with its users in English and other languages on a variety of topics, which has generated both excitement and controversy [98]. This relaxed conversation mode is friendly to the elderly and those who do not know how to use electronic devices well. If governments and researchers plan to use chatbots, the emergence of large language models like ChatGPT will bring benefits since they are suitable for answering questions and providing solutions for citizens, which can enable citizens to easily access government services.
- (2)
- NLP Applications in the Post-Pandemic Era: Another finding of ours is that COVID-19 became a topic of concern studied by many governments. Governments around the world have organised much of their work around the pandemic, and NLP can gauge the effectiveness of government policies [87]. As the illness appears less severe and has turned into a type of respiratory infection, the post-pandemic era has begun, and the emphases of governments’ work have also transformed. The disease has warned and reminded people of the importance of health and lifestyle, and therefore governments need to strengthen the management and the response to public health in the near future. Set against this background, what would be the public health issues that are worth following up on? This is a noteworthy question in the post-pandemic era. NLP approaches such as sentiment analysis and keyword summarisation can be used by governments for monitoring potential reports of infections. In addition, NLP techniques have been found to be useful in the reform of public health systems to collect citizens’ feedback and their sentiments towards the changes [99]. This can serve as a potential future research direction as healthcare systems continue to evolve after the pandemic.
- (3)
- NLP Empirical Research for Government Work: Governance and policy formulation can be viewed as the main functions of governments, and researchers have conducted different NLP studies to tackle the issues in these areas, for instance, creating a contemporary government early warning system and public policy monitoring system to assess government credit in real-time [100]. Other governance-related research includes an NLP-based e-governance platform [101,102] and decision-making systems [82,103]. Our work has identified that some government departments such as health and finance have started using NLP, and it has the potential to bring convenience to other departments, too. To this end, researchers can investigate how to discover novel approaches to improve the effectiveness or accuracy of NLP models to better assist government work, with the aim of eliminating heavy workloads and manual work in the future. To bring NLP into practice, governments must develop novel strategies and guidelines to evaluate the genuine qualitative advantages of various NLP models.
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Topic | Search Terms |
---|---|
Natural language processing | “Natural Language Processing” OR “NLP” |
AND | |
Government | “government” OR “governance” OR “public sector” OR “public administration” OR “public policy” |
Keyword 1 | Keyword 2 | Co-Occurrence Count |
---|---|---|
sentiment analysis | social media | 199 |
sentiment analysis | 135 | |
sentiment analysis | tweet | 120 |
machine learning | sentiment | 92 |
machine learning | social media | 86 |
deep learning | government | 75 |
classification | government | 65 |
machine learning | public | 64 |
classification | document | 61 |
machine learning | tweet | 58 |
Word | Frequency | Eigenvector Centrality | Degree Centrality | Closeness Centrality |
---|---|---|---|---|
government | 603 | 0.216 | 1.000 | 1.000 |
social media | 410 | 0.183 | 0.972 | 0.973 |
nlp | 406 | 0.135 | 1.000 | 1.000 |
policy | 354 | 0.185 | 0.986 | 0.986 |
COVID-19 | 298 | 0.161 | 0.901 | 0.910 |
public | 270 | 0.132 | 0.986 | 0.986 |
document | 248 | 0.065 | 0.901 | 0.910 |
sentiment analysis | 218 | 0.085 | 0.951 | 0.953 |
sentiment | 216 | 0.107 | 0.944 | 0.947 |
tweet | 184 | 0.105 | 0.880 | 0.893 |
people | 182 | 0.133 | 0.993 | 0.993 |
machine learning | 179 | 0.063 | 1.000 | 1.000 |
health | 169 | 0.352 | 0.958 | 0.959 |
social | 169 | 0.255 | 0.972 | 0.973 |
164 | 0.084 | 0.930 | 0.934 | |
service | 161 | 0.150 | 0.937 | 0.940 |
development | 152 | 0.079 | 0.993 | 0.993 |
issue | 152 | 0.082 | 0.986 | 0.986 |
citizen | 144 | 0.068 | 0.972 | 0.973 |
opinion | 136 | 0.066 | 0.915 | 0.922 |
work | 135 | 0.137 | 0.965 | 0.966 |
patient | 134 | 0.362 | 0.810 | 0.840 |
pandemic | 129 | 0.087 | 0.859 | 0.877 |
platform | 126 | 0.057 | 0.965 | 0.966 |
impact | 124 | 0.059 | 0.944 | 0.947 |
organization | 118 | 0.040 | 0.958 | 0.959 |
online | 114 | 0.051 | 0.937 | 0.940 |
community | 109 | 0.245 | 0.951 | 0.953 |
value | 106 | 0.197 | 0.930 | 0.934 |
communication | 102 | 0.082 | 0.937 | 0.940 |
Keyword 1 | Keyword 2 | Co-Occurrence Count |
---|---|---|
deep learning | government | 75 |
classification | government | 65 |
classification | document | 61 |
sentiment analysis | work | 44 |
deep learning | detection | 35 |
chatbot | service | 33 |
machine learning | policy | 31 |
extraction | government | 30 |
machine learning | platform | 28 |
chatbot | government | 26 |
Keyword 1 | Keyword 2 | Co-Occurrence Count |
---|---|---|
sentiment analysis | social media | 199 |
sentiment analysis | 135 | |
sentiment analysis | tweet | 120 |
machine learning | sentiment | 92 |
machine learning | social media | 86 |
machine learning | public | 64 |
machine learning | tweet | 58 |
opinion mining | social | 48 |
machine learning | 48 | |
classification | social media | 46 |
Keyword 1 | Keyword 2 | Co-Occurrence Count |
---|---|---|
identification | patient | 55 |
topic modeling | vaccine | 35 |
identification | impact | 31 |
sentiment analysis | vaccine | 28 |
deep learning | pandemic | 24 |
machine learning | vaccination | 22 |
machine learning | pandemic | 20 |
sentiment analysis | vaccination | 19 |
data mining | health | 17 |
extraction | health | 16 |
Keyword 1 | Keyword 2 | Co-Occurrence Count |
---|---|---|
machine learning | organization | 28 |
identification | value | 24 |
machine learning | world | 23 |
deep learning | real world | 22 |
sentiment analysis | value | 18 |
machine learning | state | 18 |
classification | disaster | 18 |
sentiment analysis | world | 13 |
identification | state | 13 |
machine learning | market | 13 |
# | Category | Description |
---|---|---|
1 | Governance and Policy | Efficiently extract and analyse documents and policies; obtain insights for better governances and services |
2 | Understanding Citizen and Public Opinion | Evaluating citizens’ sentient and public opinions; understanding attitudes regarding policies |
3 | Medical and Healthcare | Measuring the reception of health policies; clarifying the situations and the efficiency of public health measures during the pandemic |
4 | Economic and Environment | Retrieving the reactions of markets; identifying risks and threats in economics |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Jiang, Y.; Pang, P.C.-I.; Wong, D.; Kan, H.Y. Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic Review. Appl. Sci. 2023, 13, 12346. https://doi.org/10.3390/app132212346
Jiang Y, Pang PC-I, Wong D, Kan HY. Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic Review. Applied Sciences. 2023; 13(22):12346. https://doi.org/10.3390/app132212346
Chicago/Turabian StyleJiang, Yunqing, Patrick Cheong-Iao Pang, Dennis Wong, and Ho Yin Kan. 2023. "Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic Review" Applied Sciences 13, no. 22: 12346. https://doi.org/10.3390/app132212346
APA StyleJiang, Y., Pang, P. C. -I., Wong, D., & Kan, H. Y. (2023). Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic Review. Applied Sciences, 13(22), 12346. https://doi.org/10.3390/app132212346