Word embedding for social sciences: an interdisciplinary survey
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. Howe...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-12-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2562.pdf |
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| author | Akira Matsui Emilio Ferrara |
| author_facet | Akira Matsui Emilio Ferrara |
| author_sort | Akira Matsui |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5e69aa837fb44e7a9c549993ee9dbfb6 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-5e69aa837fb44e7a9c549993ee9dbfb62024-12-07T15:05:16ZengPeerJ Inc.PeerJ Computer Science2376-59922024-12-0110e256210.7717/peerj-cs.2562Word embedding for social sciences: an interdisciplinary surveyAkira Matsui0Emilio Ferrara1College of Business Administration, Yokohama National University, Yokohama, Kanagawa, JapanThomas Lord Department of Computer Science, University of Southern California, Los Angeles, California, United StatesMachine 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.https://peerj.com/articles/cs-2562.pdfWord embeddingword2vecComputational social scienceBias in machine learning |
| spellingShingle | Akira Matsui Emilio Ferrara Word embedding for social sciences: an interdisciplinary survey PeerJ Computer Science Word embedding word2vec Computational social science Bias in machine learning |
| title | Word embedding for social sciences: an interdisciplinary survey |
| title_full | Word embedding for social sciences: an interdisciplinary survey |
| title_fullStr | Word embedding for social sciences: an interdisciplinary survey |
| title_full_unstemmed | Word embedding for social sciences: an interdisciplinary survey |
| title_short | Word embedding for social sciences: an interdisciplinary survey |
| title_sort | word embedding for social sciences an interdisciplinary survey |
| topic | Word embedding word2vec Computational social science Bias in machine learning |
| url | https://peerj.com/articles/cs-2562.pdf |
| work_keys_str_mv | AT akiramatsui wordembeddingforsocialsciencesaninterdisciplinarysurvey AT emilioferrara wordembeddingforsocialsciencesaninterdisciplinarysurvey |