Performing sentiment analysis to trace the history of identity and belonging in ancient Greek literature

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3A853QDVES" target="_blank" >RIV/00216208:11320/26:853QDVES - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209547307&doi=10.1093%2Fllc%2Ffqae048&partnerID=40&md5=d6fc5983eb6bbdb287f850d7a5848cb8" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209547307&doi=10.1093%2Fllc%2Ffqae048&partnerID=40&md5=d6fc5983eb6bbdb287f850d7a5848cb8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1093/llc/fqae048" target="_blank" >10.1093/llc/fqae048</a>

Result language
angličtina
Original language name
Performing sentiment analysis to trace the history of identity and belonging in ancient Greek literature
Original language description
This paper presents a Natural Language Processing approach for the in-depth study of ancient Greek literature. Specifically, our project mines the largest data collection of ancient Greek literature to extract descriptive words and their contextual collocations. We then present the Python libraries developed to perform sentiment analysis with a focus on words about gender, race, religion, ethnicity, and other distinguishable characteristics and how they were used by Greek authors. Finally, we showcase some initial findings as a proof of concept. © 2024 Elsevier B.V., All rights reserved.
Czech name
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Czech description
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Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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