@InProceedings{cortis-davis:2022:SIGUL,
  author    = {Cortis, Keith  and  Davis, Brian},
  title     = {Baseline English and Maltese-English Classification Models for Subjectivity Detection, Sentiment Analysis, Emotion Analysis, Sarcasm Detection, and Irony Detection},
  booktitle      = {Proceedings of the the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages},
  month          = {June},
  year           = {2022},
  address        = {Marseille, France},
  publisher      = {European Language Resources Association},
  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, Naïve 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.},
  url       = {https://aclanthology.org/2022.sigul-1.21}
}