python-webis: Python wrapper for the webis Twitter sentiment evaluation ensemble

Forskningsoutput: Icke-textbaserad outputProgramvaraVetenskaplig

Sammanfattning

This is a Python wrapper around the Java implementation of a Twitter sentiment evaluation framework presented by Hagen et al. (2015). In Zimbra et al. (2018)’s evaluation of “The State-of-the-Art in Twitter Sentiment Analysis” this approach received the highest score in classification accuracy (average across five categories of content, table 4, p5:15).

This package is available from the PyPi software repository: https://pypi.org/project/webis/
Its source code is hosted on GitLab: https://gitlab.com/christoph.fink/python-webis
Originalspråkengelska
Utgivningsformatinternet
Storlek40kB
DOI
StatusPublicerad - 23 jan 2019
MoE-publikationstypI2 ICT-programvara

Vetenskapsgrenar

  • 1172 Miljövetenskap
  • 6160 Övriga humanistiska vetenskaper

Citera det här

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abstract = "This is a Python wrapper around the Java implementation of a Twitter sentiment evaluation framework presented by Hagen et al. (2015). In Zimbra et al. (2018)’s evaluation of “The State-of-the-Art in Twitter Sentiment Analysis” this approach received the highest score in classification accuracy (average across five categories of content, table 4, p5:15).This package is available from the PyPi software repository: https://pypi.org/project/webis/ Its source code is hosted on GitLab: https://gitlab.com/christoph.fink/python-webis",
keywords = "1172 Environmental sciences, 6160 Other humanities",
author = "Fink, {Christoph Alexander}",
year = "2019",
month = "1",
day = "23",
doi = "10.5281/zenodo.2547461",
language = "English",

}

python-webis: Python wrapper for the webis Twitter sentiment evaluation ensemble. Fink, Christoph Alexander (Författare). 2019.

Forskningsoutput: Icke-textbaserad outputProgramvaraVetenskaplig

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AB - This is a Python wrapper around the Java implementation of a Twitter sentiment evaluation framework presented by Hagen et al. (2015). In Zimbra et al. (2018)’s evaluation of “The State-of-the-Art in Twitter Sentiment Analysis” this approach received the highest score in classification accuracy (average across five categories of content, table 4, p5:15).This package is available from the PyPi software repository: https://pypi.org/project/webis/ Its source code is hosted on GitLab: https://gitlab.com/christoph.fink/python-webis

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KW - 6160 Other humanities

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