## 4 Results
### Co-Word and Network Analysis  
The co-occurrence relationship between two keywords refers to the degree of connection between two concepts when they are represented in a network data structure as source and target nodes. The co-occurrence count depicts the strength of the connection between a pair of words. Table 2 illustrates the top 10 co-occurrence relationships between NLP techniques and the keywords of the literature. Only 10 word pairs are listed due to space constraints. As shown in the table, \"sentiment analysis\" has a strong relationship with \"social media\", \"twitter\", \"tweet\", showing its applications on social media platforms; \"machine learning\" is listed with its application areas of \"sentiment\", \"social media\", \"public\", and \"tweet\"; \"deep learning\" is associated with \"government\"; and \"classification\" is related to \"government\" and \"document\", which illustrates the popular use of deep learning for government document processing. Figure 3: Word cloud of the literature on NLP in government. Figure 2: Research publications on NLP in government over time. Figure 4 presents the network made with the pairs of co-words, which contained 143 nodes (representing research keywords) and 8293 relations (i.e., edges in the network indicating pairs of keywords co-appearing in all data). The density of the co-word network was measured at 0.831. To better visualise related concepts, several coloured nodes are defined: NLP techniques are represented by yellow nodes, red nodes stand for \"governance and policy\", orange nodes refer to \"citizens and public opinion\", green nodes are designated for \"medical and healthcare\", and blue nodes represent \"economy and environment\". The frequencies of words appearing in the dataset determine the sizes of nodes, and the words with larger nodes (such as \"government\", \"nlp\") reflect the key ideas in the corpus of the literature. The co-word visualisation can clearly show the overview of the NLP-related literature in government and public administration research. As shown in the figure, keywords such as \"sentiment analysis\", \"machine learning\", \"classification\", \"deep learning\", \"artificial intelligence\", \"identification\", \"extraction\", \"text mining\", \"topic modeling\", \"prediction\", \"recognition\", \"information retrieval\", \"text classification\", \"chatbot\", \"opinion mining\", and \"question answering\" are the NLP techniques mostly found in the literature. The measurement of the centralities of nodes is another accurate method for determining what is crucial within a network [64]. The results of frequency counts, eigenvector centralities, degree centralities, and closeness centralities of the top 30 keywords are shown in Table 3. The frequency counts were used to order the keywords in the table. When two or more keywords have the same occurrence value, the one with a higher degree of centrality is seen to be more important. Table 3 shows that the most influential concepts include \"government\", \"social media\", and \"nlp\". Moreover, the result demonstrates that the keywords \"health\", \"social\", \"patient\", and \"community\" have relatively high centrality values, despite their lower frequencies. These can be the research areas connecting other government research areas and NLP techniques in recent years. Besides the analysis of the overall network (Figure 4), a detailed analysis of different research directions is also significant as it may lead to more undiscovered and interesting insights. The network visualisations and the co-occurrence matrixes between NLP techniques and different research directions are displayed below. Figure 5 demonstrates the relationships of co-occurrence between the keywords of NLP techniques and the \"governance and policy\" direction. This research direction contains keywords such as \"government\", \"policy\", \"document\", \"service\", \"development\", \"communication\", \"decision-making\", and \"response\". Table 4 describes the top 10 popular NLP applications in this direction.