@inproceedings{garain-etal-2020-junlp,
    title = "{JUNLP} at {S}em{E}val-2020 Task 9: Sentiment Analysis of {H}indi-{E}nglish Code Mixed Data Using Grid Search Cross Validation",
    author = "Garain, Avishek  and
      Mahata, Sainik  and
      Das, Dipankar",
    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/Add-Cong-Liu-Florida-Atlantic-University-author-id/2020.semeval-1.171/",
    doi = "10.18653/v1/2020.semeval-1.171",
    pages = "1276--1280",
    abstract = "Code-mixing is a phenomenon which arises mainly in multilingual societies. Multilingual people, who are well versed in their native languages and also English speakers, tend to code-mix using English-based phonetic typing and the insertion of anglicisms in their main language. This linguistic phenomenon poses a great challenge to conventional NLP domains such as Sentiment Analysis, Machine Translation, and Text Summarization, to name a few. In this work, we focus on working out a plausible solution to the domain of Code-Mixed Sentiment Analysis. This work was done as participation in the SemEval-2020 Sentimix Task, where we focused on the sentiment analysis of English-Hindi code-mixed sentences. our username for the submission was {\textquotedblleft}sainik.mahata{\textquotedblright} and team name was {\textquotedblleft}JUNLP{\textquotedblright}. We used feature extraction algorithms in conjunction with traditional machine learning algorithms such as SVR and Grid Search in an attempt to solve the task. Our approach garnered an f1-score of 66.2{\%} when tested using metrics prepared by the organizers of the task."
}