@inproceedings{jabreel-moreno-2018-eitaka,
    title = "{E}i{TAKA} at {S}em{E}val-2018 Task 1: An Ensemble of N-Channels {C}onv{N}et and {XG}boost Regressors for Emotion Analysis of Tweets",
    author = "Jabreel, Mohammed  and
      Moreno, Antonio",
    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/S18-1029/",
    doi = "10.18653/v1/S18-1029",
    pages = "193--199",
    abstract = "This paper describes our system that has been used in Task1 Affect in Tweets. We combine two different approaches. The first one called N-Stream ConvNets, which is a deep learning approach where the second one is XGboost regressor based on a set of embedding and lexicons based features. Our system was evaluated on the testing sets of the tasks outperforming all other approaches for the Arabic version of valence intensity regression task and valence ordinal classification task."
}