@inproceedings{cortis-davis-2022-baseline,
    title = "Baseline {E}nglish and {M}altese-{E}nglish Classification Models for Subjectivity Detection, Sentiment Analysis, Emotion Analysis, Sarcasm Detection, and Irony Detection",
    author = "Cortis, Keith  and
      Davis, Brian",
    editor = "Melero, Maite  and
      Sakti, Sakriani  and
      Soria, Claudia",
    booktitle = "Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.sigul-1.21/",
    pages = "161--168",
    abstract = {This paper presents baseline classification models for subjectivity detection, sentiment analysis, emotion analysis, sarcasm detection, and irony detection. All models are trained on user-generated content gathered from newswires and social networking services, in three different languages: English {---}a high-resourced language, Maltese {---}a low-resourced language, and Maltese-English {---}a code-switched language. Traditional supervised algorithms namely, Support Vector Machines, Na{\"i}ve Bayes, Logistic Regression, Decision Trees, and Random Forest, are used to build a baseline for each classification task, namely subjectivity, sentiment polarity, emotion, sarcasm, and irony. Baseline models are established at a monolingual (English) level and at a code-switched level (Maltese-English). Results obtained from all the classification models are presented.}
}