@inproceedings{baruah-etal-2020-iiitg-adbu,
    title = "{IIITG}-{ADBU} at {S}em{E}val-2020 Task 9: {SVM} for Sentiment Analysis of {E}nglish-{H}indi Code-Mixed Text",
    author = "Baruah, Arup  and
      Das, Kaushik  and
      Barbhuiya, Ferdous  and
      Dey, Kuntal",
    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/ingest-emnlp/2020.semeval-1.121/",
    doi = "10.18653/v1/2020.semeval-1.121",
    pages = "946--950",
    abstract = "In this paper, we present the results that the team IIITG-ADBU (codalab username `abaruah') obtained in the SentiMix task (Task 9) of the International Workshop on Semantic Evaluation 2020 (SemEval 2020). This task required the detection of sentiment in code-mixed Hindi-English tweets. Broadly, we performed two sets of experiments for this task. The first experiment was performed using the multilingual BERT classifier and the second set of experiments was performed using SVM classifiers. The character-based SVM classifier obtained the best F1 score of 0.678 in the test set with a rank of 21 among 62 participants. The performance of the multilingual BERT classifier was quite comparable with the SVM classifier on the development set. However, on the test set it obtained an F1 score of 0.342."
}