论文标题
机器学习，自然语言处理分析青年观点：可持续青年发展政策的关键趋势和重点领域
A Machine Learning, Natural Language Processing Analysis of Youth Perspectives: Key Trends and Focus Areas for Sustainable Youth Development Policies
论文作者
论文摘要
投资儿童和青少年是迈向当前和后代的包容，公平和可持续发展的关键一步。实现共同的全球目标的一些国际议程强调了积极的青年参与和可持续发展的参与的必要性。 2030年的可持续发展议程强调了青年参与的需求，并将青年观点包括在于解决17个可持续发展目标的重要一步。这项研究的目的是分析青年观点，价值观和情感，以通过使用机器学习通过社交网络分析来解决17个可持续发展目标解决的问题。在7个主要可持续性会议期间收集的社交网络数据旨在使用自然语言处理技术进行情感分析，以分析旨在吸引儿童和青少年的社交网络数据。该数据使用在7个青年可持续性会议期间在社交网络数据的样本数据集上培训的自然语言处理文本分类器进行分类，以更深入地了解与可持续发展目标有关的青年观点。机器学习确定的人口和位置属性和特征被利用以确定年龄，性别和种族之间的偏见和人口差异。使用自然语言处理，从7个不同国家 /地区收集的3种语言的定性数据是系统地翻译，分类和分析的，揭示了可持续青年发展政策的关键趋势和重点领域。获得的结果揭示了一般青年对可持续发展的知识深度及其对17个可持续发展目标中每一种的态度。这项研究的结果是更好地了解儿童和青少年在实现2030年目标目标中的利益，角色和观点的指南。
Investing in children and youth is a critical step towards inclusive, equitable, and sustainable development for current and future generations. Several international agendas for accomplishing common global goals emphasize the need for active youth participation and engagement for sustainable development. The 2030 Agenda for Sustainable Development emphasizes the need for youth engagement and the inclusion of youth perspectives as an important step toward addressing each of the 17 Sustainable Development Goals. The aim of this study is to analyze youth perspectives, values, and sentiments towards issues addressed by the 17 Sustainable Development Goals through social network analysis using machine learning. Social network data collected during 7 major sustainability conferences aimed at engaging children and youth is analyzed using natural language processing techniques for sentiment analysis. This data categorized using a natural language processing text classifier trained on a sample dataset of social network data during the 7 youth sustainability conferences for deeper understanding of youth perspectives in relation to the SDGs. Machine learning identified demographic and location attributes and features are utilized in order to identify bias and demographic differences between ages, gender, and race among youth. Using natural language processing, the qualitative data collected from over 7 different countries in 3 languages are systematically translated, categorized, and analyzed, revealing key trends and focus areas for sustainable youth development policies. The obtained results reveal the general youth's depth of knowledge on sustainable development and their attitudes towards each of the 17 SDGs. The findings of this study serve as a guide toward better understanding the interests, roles, and perspectives of children and youth in achieving the goals of Agenda 2030.
