Matsui Akira, Ferrara Emilio
College of Business Administration, Yokohama National University, Yokohama, Kanagawa, Japan.
Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, California, United States.
PeerJ Comput Sci. 2024 Dec 5;10:e2562. doi: 10.7717/peerj-cs.2562. eCollection 2024.
Machine learning models learn low-dimensional representations from complex high-dimensional data. Not only computer science but also social science has benefited from the advancement of these powerful tools. Within such tools, word embedding is one of the most popular methods in the literature. However, we have no particular documentation of this emerging trend because this trend overlaps different social science fields. To well compile this fragmented knowledge, we survey recent studies that apply word embedding models to human behavior mining. Our taxonomy built on the surveyed article provides a concise but comprehensive overview of this emerging trend of intersection between computer science and social science and guides scholars who are going to navigate the use of word embedding algorithms in their voyage of social science research.
机器学习模型从复杂的高维数据中学习低维表示。不仅计算机科学，社会科学也从这些强大工具的发展中受益。在这些工具中，词嵌入是文献中最流行的方法之一。然而，我们没有关于这一新兴趋势的具体文献记录，因为这一趋势跨越了不同的社会科学领域。为了很好地整合这些零散的知识，我们调查了最近将词嵌入模型应用于人类行为挖掘的研究。我们基于所调查文章构建的分类法对计算机科学和社会科学交叉的这一新兴趋势提供了简洁而全面的概述，并指导学者在其社会科学研究之旅中运用词嵌入算法。
