@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://preview.aclanthology.org/jlcl-multiple-ingestion/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 {\textquotedblleft}Classification of COVID19 tweets containing symptoms{\textquotedblright} shared task, organized by the {\textquotedblleft}Social Media Mining for Health Applications (SMM4H){\textquotedblright} 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."
}