@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",
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
%S Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
%D 2021
%8 jun
%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