@inproceedings{de-bruyne-etal-2018-lt3,
    title = "{LT}3 at {S}em{E}val-2018 Task 1: A classifier chain to detect emotions in tweets",
    author = "De Bruyne, Luna  and
      De Clercq, Orph{\'e}e  and
      Hoste, V{\'e}ronique",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/S18-1016/",
    doi = "10.18653/v1/S18-1016",
    pages = "123--127",
    abstract = "This paper presents an emotion classification system for English tweets, submitted for the SemEval shared task on Affect in Tweets, subtask 5: Detecting Emotions. The system combines lexicon, n-gram, style, syntactic and semantic features. For this multi-class multi-label problem, we created a classifier chain. This is an ensemble of eleven binary classifiers, one for each possible emotion category, where each model gets the predictions of the preceding models as additional features. The predicted labels are combined to get a multi-label representation of the predictions. Our system was ranked eleventh among thirty five participating teams, with a Jaccard accuracy of 52.0{\%} and macro- and micro-average F1-scores of 49.3{\%} and 64.0{\%}, respectively."
}