Unsupervised Learning-based Approach for Contextual Understanding of Web Material Around a New Domain of Algorithmic Government

Abstract

Grasping the contextual nuances is fundamental for efficacious learning in a novel discipline through internet-based research. Such comprehension significantly augments the decision-making process by promoting well-grounded and informed choices. And with advent of machine learning approaches, it becomes even more fast and robust to enable collaboration between machine algorithms and humans. However, human expertise still holds the key for new domain, which has been proposed in this study as a key step in unsupervised learning approach of k -means clustering technique. Domain search term and context terms for the new domain are added to the clustering technique, and the relevance of the resultant groups has been tested. Context setting helps to analyse and understand the content of documents and other sources of information. For a new domain like algorithmic government, which does not have many documents on the web, it was found that contextual learning was up to 40% more relevant than the normal learning approach. The qualitative aspect of the clusters was found much better by the experts than quantitative aspect due to availability of lesser number of search documents. It was found that scientific research also supports the groups formed during contextual learning approach. This approach should help government to better understand and respond to the needs and concerns of their citizens by deriving better data insights in more quickly and to make more informed, evidence-based decisions that are sensitive to the needs and values of different communities and stakeholders. And thus, many stakeholders in the new domain can use this approach for exploration, research, policy formulation, strategizing, implementing, and testing of the various learned concepts. A total of 15 search engines were used in the experimental settings with thousands of web crawling being done using the Carrot 2 engine. Text embedding was done using bag-of-word technique, and k -means clustering was implemented for producing 25 clusters across the two types of learnings.