Abstract
AbstractThe aim of this study is to explore how parenthood and birth rate is manifested in Finnish society and citizens as revealed by automated mining of news articles from Finland News API. Several levels of analysis were conducted using natural language processing and text mining techniques to unfold relevant insights from the collected News API. This includes surface-level analysis, word frequency-based analysis, topic-based analysis, and health ontology mapping. In total, 1621 news articles were selected and analyzed from the collected dataset. The surface-level analysis revealed the capacity of imminent health researchers to gain public audience and interest. Content-based analysis revealed the importance of family, employment, and health issues. Topical analysis stressed on the dominance of family issues during corona time, followed by public services and employment issues. Finally, the health ontology mapping revealed the quasi dominance of mental health and development disorders concerns. The research work provides a general framework for analyzing unstructured text to extract useful insights that can help policymakers to positively impact the existing policy in health and social policy development.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science
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