@inproceedings{zhang-etal-2018-nlpzzx,
    title = "{NLPZZX} at {S}em{E}val-2018 Task 1: Using Ensemble Method for Emotion and Sentiment Intensity Determination",
    author = "Zhang, Zhengxin  and
      Zhou, Qimin  and
      Wu, Hao",
    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/jlcl-multiple-ingestion/S18-1015/",
    doi = "10.18653/v1/S18-1015",
    pages = "116--122",
    abstract = "In this paper, we put forward a system that competed at SemEval-2018 Task 1: {\textquotedblleft}Affect in Tweets{\textquotedblright}. Our system uses a simple yet effective ensemble method which combines several neural network components. We participate in two subtasks for English tweets: EI-reg and V-reg. For two subtasks, different combinations of neural components are examined. For EI-reg, our system achieves an accuracy of 0.727 in Pearson Correlation Coefficient (all instances) and an accuracy of 0.555 in Pearson Correlation Coefficient (0.5-1). For V-reg, the achieved accuracy scores are respectively 0.835 and 0.670"
}