@inproceedings{yeh-etal-2017-ncyu,
    title = "{NCYU} at {IJCNLP}-2017 Task 2: Dimensional Sentiment Analysis for {C}hinese Phrases using Vector Representations",
    author = "Yeh, Jui-Feng  and
      Tsai, Jian-Cheng  and
      Wu, Bo-Wei  and
      Kuang, Tai-You",
    editor = "Liu, Chao-Hong  and
      Nakov, Preslav  and
      Xue, Nianwen",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://preview.aclanthology.org/fix-sig-urls/I17-4018/",
    pages = "112--117",
    abstract = "This paper presents two vector representations proposed by National Chiayi University (NCYU) about phrased-based sentiment detection which was used to compete in dimensional sentiment analysis for Chinese phrases (DSACP) at IJCNLP 2017. The vector-based sentiment phraselike unit analysis models are proposed in this article. E-HowNet-based clustering is used to obtain the values of valence and arousal for sentiment words first. An out-of-vocabulary function is also defined in this article to measure the dimensional emotion values for unknown words. For predicting the corresponding values of sentiment phrase-like unit, a vectorbased approach is proposed here. According to the experimental results, we can find the proposed approach is efficacious."
}