@inproceedings{parikh-etal-2020-irlab,
    title = "{IRL}ab{\_}{DAIICT} at {S}em{E}val-2020 Task 9: Machine Learning and Deep Learning Methods for Sentiment Analysis of Code-Mixed Tweets",
    author = "Parikh, Apurva  and
      Bisht, Abhimanyu Singh  and
      Majumder, Prasenjit",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.semeval-1.169/",
    doi = "10.18653/v1/2020.semeval-1.169",
    pages = "1265--1269",
    abstract = "The paper describes systems that our team IRLab{\_}DAIICT employed for the shared task Sentiment Analysis for Code-Mixed Social Media Text in SemEval 2020. We conducted our experiments on a Hindi-English CodeMixed Tweet dataset which was annotated with sentiment labels. F1-score was the official evaluation metric and our best approach, an ensemble of Logistic Regression, Random Forest and BERT, achieved an F1-score of 0.693."
}