@inproceedings{aparaschivei-etal-2020-fii,
    title = "{FII}-{UAIC} at {S}em{E}val-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using {CNN}",
    author = "Aparaschivei, Lavinia  and
      Palihovici, Andrei  and
      G{\^i}fu, Daniela",
    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-1.118/",
    doi = "10.18653/v1/2020.semeval-1.118",
    pages = "928--933",
    abstract = "The {\textquotedblleft}Sentiment Analysis for Code-Mixed Social Media Text{\textquotedblright} task at the SemEval 2020 competition focuses on sentiment analysis in code-mixed social media text , specifically, on the combination of English with Spanish (Spanglish) and Hindi (Hinglish). In this paper, we present a system able to classify tweets, from Spanish and English languages, into positive, negative and neutral. Firstly, we built a classifier able to provide corresponding sentiment labels. Besides the sentiment labels, we provide the language labels at the word level. Secondly, we generate a word-level representation, using Convolutional Neural Network (CNN) architecture. Our solution indicates promising results for the Sentimix Spanglish-English task (0.744), the team, Lavinia{\_}Ap, occupied the 9th place. However, for the Sentimix Hindi-English task (0.324) the results have to be improved."
}