article · Intersections · 2023
Natural language processing
Cited by 6Open access
Abstract
Natural language processing (NLP) methods are designed to automatically process and analyze large amounts of textual data. The integration of this new-generation toolbox into sociology faces many challenges. NLP was institutionalized outside of sociology, while the expertise of sociology has been based on its own methods of research. Another challenge is epistemological: it is related to the validity of digital data and the different viewpoints associated with predictive and causal approaches. In our paper, we discuss the challenges and opportunities of the use of NLP in sociology, offer some potential solutions to the concerns and provide meaningful and diverse examples of its sociological application, most of which are related to research on Eastern European societies. The focus will be on the use of NLP in quantitative text analysis. Solutions are provided concerning how sociological knowledge can be incorporated into the new methods and how the new analytical tools can be evaluated against the principles of traditional quantitative methodology.
Cite this paper
Németh, R., & Koltai, J. (2023). Natural language processing. Intersections, 9(1), 5–22. https://doi.org/10.17356/ieejsp.v9i1.871
Németh, Renáta, and Júlia Koltai. “Natural language processing.” Intersections, vol. 9, no. 1, 2023, pp. 5–22, https://doi.org/10.17356/ieejsp.v9i1.871.
Németh, Renáta, and Júlia Koltai. 2023. “Natural language processing.” Intersections 9 (1): 5–22. https://doi.org/10.17356/ieejsp.v9i1.871.
Németh, R. and Koltai, J. (2023) ‘Natural language processing’, Intersections, 9(1), pp. 5–22. Available at: https://doi.org/10.17356/ieejsp.v9i1.871.
@article{nemeth2023natural,
title = {{Natural language processing}},
author = {Németh, Renáta and Koltai, Júlia},
journal = {Intersections},
volume = {9},
number = {1},
pages = {5--22},
year = {2023},
doi = {10.17356/ieejsp.v9i1.871}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.
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