@inproceedings{wang-etal-2017-eica,
    title = "{EICA} at {S}em{E}val-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification",
    author = "Wang, Maoquan  and
      Chen, Shiyun  and
      Xie, Yufei  and
      Zhao, Lu",
    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/iwcs-25-ingestion/S17-2124/",
    doi = "10.18653/v1/S17-2124",
    pages = "737--740",
    abstract = "This paper describes our approach for SemEval-2017 Task 4 - Sentiment Analysis in Twitter (SAT). Its five subtasks are divided into two categories: (1) sentiment classification, i.e., predicting topic-based tweet sentiment polarity, and (2) sentiment quantification, that is, estimating the sentiment distributions of a set of given tweets. We build a convolutional sentence classification system for the task of SAT. Official results show that the experimental results of our system are comparative."
}