@inproceedings{mondal-etal-2021-classification,
title = "Classification of {COVID}19 tweets using Machine Learning Approaches",
author = "Mondal, Anupam and
Mahata, Sainik and
Dey, Monalisa and
Das, Dipankar",
editor = "Magge, Arjun and
Klein, Ari and
Miranda-Escalada, Antonio and
Al-garadi, Mohammed Ali and
Alimova, Ilseyar and
Miftahutdinov, Zulfat and
Farre-Maduell, Eulalia and
Lopez, Salvador Lima and
Flores, Ivan and
O'Connor, Karen and
Weissenbacher, Davy and
Tutubalina, Elena and
Sarker, Abeed and
Banda, Juan M and
Krallinger, Martin and
Gonzalez-Hernandez, Graciela",
booktitle = "Proceedings of the Sixth Social Media Mining for Health ({\#}SMM4H) Workshop and Shared Task",
month = jun,
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.smm4h-1.29",
doi = "10.18653/v1/2021.smm4h-1.29",
pages = "135--137",
abstract = "The reported work is a description of our participation in the {``}Classification of COVID19 tweets containing symptoms{''} shared task, organized by the {``}Social Media Mining for Health Applications (SMM4H){''} workshop. The literature describes two machine learning approaches that were used to build a three class classification system, that categorizes tweets related to COVID19, into three classes, viz., self-reports, non-personal reports, and literature/news mentions. The steps for pre-processing tweets, feature extraction, and the development of the machine learning models, are described extensively in the documentation. Both the developed learning models, when evaluated by the organizers, garnered F1 scores of 0.93 and 0.92 respectively.",
}
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<abstract>The reported work is a description of our participation in the “Classification of COVID19 tweets containing symptoms” shared task, organized by the “Social Media Mining for Health Applications (SMM4H)” workshop. The literature describes two machine learning approaches that were used to build a three class classification system, that categorizes tweets related to COVID19, into three classes, viz., self-reports, non-personal reports, and literature/news mentions. The steps for pre-processing tweets, feature extraction, and the development of the machine learning models, are described extensively in the documentation. Both the developed learning models, when evaluated by the organizers, garnered F1 scores of 0.93 and 0.92 respectively.</abstract>
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%0 Conference Proceedings
%T Classification of COVID19 tweets using Machine Learning Approaches
%A Mondal, Anupam
%A Mahata, Sainik
%A Dey, Monalisa
%A Das, Dipankar
%Y Magge, Arjun
%Y Klein, Ari
%Y Miranda-Escalada, Antonio
%Y Al-garadi, Mohammed Ali
%Y Alimova, Ilseyar
%Y Miftahutdinov, Zulfat
%Y Farre-Maduell, Eulalia
%Y Lopez, Salvador Lima
%Y Flores, Ivan
%Y O’Connor, Karen
%Y Weissenbacher, Davy
%Y Tutubalina, Elena
%Y Sarker, Abeed
%Y Banda, Juan M.
%Y Krallinger, Martin
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F mondal-etal-2021-classification
%X The reported work is a description of our participation in the “Classification of COVID19 tweets containing symptoms” shared task, organized by the “Social Media Mining for Health Applications (SMM4H)” workshop. The literature describes two machine learning approaches that were used to build a three class classification system, that categorizes tweets related to COVID19, into three classes, viz., self-reports, non-personal reports, and literature/news mentions. The steps for pre-processing tweets, feature extraction, and the development of the machine learning models, are described extensively in the documentation. Both the developed learning models, when evaluated by the organizers, garnered F1 scores of 0.93 and 0.92 respectively.
%R 10.18653/v1/2021.smm4h-1.29
%U https://aclanthology.org/2021.smm4h-1.29
%U https://doi.org/10.18653/v1/2021.smm4h-1.29
%P 135-137
Markdown (Informal)
[Classification of COVID19 tweets using Machine Learning Approaches](https://aclanthology.org/2021.smm4h-1.29) (Mondal et al., SMM4H 2021)
ACL