@inproceedings{zhou-etal-2017-ecnu,
    title = "{ECNU} at {S}em{E}val-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for {T}witter Message Polarity Classification",
    author = "Zhou, Yunxiao  and
      Lan, Man  and
      Wu, Yuanbin",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      Cer, Daniel  and
      Jurgens, David",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/S17-2137/",
    doi = "10.18653/v1/S17-2137",
    pages = "812--816",
    abstract = "This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i.e., Message Polarity Classification. We investigated several traditional Natural Language Processing (NLP) features, domain specific features and word embedding features together with supervised machine learning methods to address this task. Officially released results showed that our system ranked above average."
}