机器学习、对青年观点的自然语言处理分析: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)

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.

翻译：2030年《可持续发展议程》强调青年参与和纳入青年观点的必要性,作为解决17项可持续发展目标中每一项目标的一个重要步骤。本研究的目的是通过利用机器学习分析社会网络分析,分析青年的观点、价值观和对17项可持续发展目标所处理问题的看法。利用自然语言处理技术,对7次主要可持续性会议期间收集的、旨在使儿童和青年参与的社会网络数据进行分析,以进行情绪分析。这些数据使用自然语言处理技术进行分类,在7次青年可持续性会议期间,就社会网络数据抽样数据集进行了培训,以便更深入地了解青年观点与可持续发展目标的关系。机械学习确定了人口和地点属性和特征,用于查明青年年龄、性别和种族之间的偏见和人口差异。利用自然语言处理,系统翻译、分类和分析从7个不同国家收集的3种语言社会网络数据,揭示关键趋势和重点领域,以了解情绪分析情绪分析。这些数据使用自然语言处理文本分类,在7次青年可持续性会议期间对社会网络数据进行分类,以便更深入地了解与可持续发展目标有关的青年观点。通过机器学习,了解各项可持续发展目标的成果,并了解17项可持续发展目标。