@inproceedings{advani-etal-2020-c1,
    title = "C1 at {S}em{E}val-2020 Task 9: {S}enti{M}ix: Sentiment Analysis for Code-Mixed Social Media Text Using Feature Engineering",
    author = "Advani, Laksh  and
      Lu, Clement  and
      Maharjan, Suraj",
    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/Author-Pages-WenzhengZhang-ZhengyanShi-ShuYang/2020.semeval-1.163/",
    doi = "10.18653/v1/2020.semeval-1.163",
    pages = "1227--1232",
    abstract = "In today{'}s interconnected and multilingual world, code-mixing of languages on social media is a common occurrence. While many Natural Language Processing (NLP) tasks like sentiment analysis are mature and well designed for monolingual text, techniques to apply these tasks to code-mixed text still warrant exploration. This paper describes our feature engineering approach to sentiment analysis in code-mixed social media text for SemEval-2020 Task 9: SentiMix. We tackle this problem by leveraging a set of hand-engineered lexical, sentiment, and metadata fea- tures to design a classifier that can disambiguate between ``positive'', ``negative'' and ``neutral'' sentiment. With this model we are able to obtain a weighted F1 score of 0.65 for the ``Hinglish'' task and 0.63 for the ``Spanglish'' tasks."
}