Word embedding for social sciences: an interdisciplinary survey

DOI PDF Web Site XML 参考文献123件 オープンアクセス
  • Emilio Ferrara
    Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, California, United States
  • Akira Matsui
    College of Business Administration, Yokohama National University, Yokohama, Kanagawa, Japan

書誌事項

公開日
2024-12-05
資源種別
journal article
権利情報
  • https://creativecommons.org/licenses/by/4.0/
DOI
  • 10.7717/peerj-cs.2562
公開者
PeerJ

説明

<jats:p>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.</jats:p>

収録刊行物

参考文献 (123)*注記

もっと見る

関連プロジェクト

もっと見る

詳細情報 詳細情報について

問題の指摘

ページトップへ