https://github.com/ikergarcia1996/ikergazte-covid-twitter-2021

Twitterreko Euskal Komunitatearen Eduki Azterketa Pandemia Garaian
https://github.com/ikergarcia1996/ikergazte-covid-twitter-2021

basquecountry covid covid-19 covid19 euskera ikergazte jupyter-notebook natural-language-processing nlp python twitter

Last synced: 12 days ago
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Twitterreko Euskal Komunitatearen Eduki Azterketa Pandemia Garaian

• Host: GitHub
• URL: https://github.com/ikergarcia1996/ikergazte-covid-twitter-2021
• Owner: ikergarcia1996
• License: apache-2.0
• Created: 2021-04-23T10:16:56.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-04-23T10:25:39.000Z (over 3 years ago)
• Last Synced: 2024-12-06T17:00:14.872Z (28 days ago)
• Topics: basquecountry, covid, covid-19, covid19, euskera, ikergazte, jupyter-notebook, natural-language-processing, nlp, python, twitter
• Language: Jupyter Notebook
• Homepage:
• Size: 1000 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Twitterreko Euskal Komunitatearen Eduki Azterketa Pandemia Garaian

[EU] Hizkuntzaren Prozesamenduak eskaintzen dituen teknika ez-gainbegiratuak erabiliz Twittereko euskal komunitatean COVID-19aren pandemiak izan duen eragina aztertzea da lan honen asmoa. Azterketa hau aurrera eramateko sare sozial horretako erabiltzaileen euskarazko txioak masiboki bildu eta denboraren arabera ordenatu dira. Pandemiaren eragina neurtzeko, denboraren arabera edukia nola aldatu den aztertu da, horretarako testuetan azaltzen diren hitz zein emojien aldaketa kuantitatibo zein kualitatiboak baliatu dira. Azterketa kuantitatiboan, terminoek garai desberdinetan izan duten maiztasunaren aldaketari erreparatu zaio, maiztasunen erregresio lineala erabiliz. Azterketa kualitatiboan, hitzen bektore trinkoak baliatu dira, pandemiaren garai desberdinetan hitz eta emoji adierazgarrienek esanahian izan duten bilakaera aztertzeko.

[EN] The aim of this work is to study the impact of the COVID-19 pandemic on the Basque Twitter community using unsupervised techniques based on Natural Language Processing. In order to carry out this study, large quantities of tweets were gathered and sorted by time from Basque Twitter users. To analyze the impact of the pandemic, the variability of the content over time has been studied, through quantitative and qualitative changes in the words and emojis that appear in the texts. In the quantitative analysis, the shift at the frequency of the terms was calculated using linear regression over frequencies. In the qualitative analysis, Word Embeddings were used to study the changes in the meaning of the most significant words and emojis at different times during the pandemic.

  

  

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Joseba Fernandez de Landa

Iker García

Ander Salaberria

Jon Ander Campos

HiTZ Zentroa - Ixa, Euskal Herriko Unibertsitatea (UPV/EHU)

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