@inproceedings{he-etal-2017-yzu,
    title = "{YZU}-{NLP} at {E}mo{I}nt-2017: Determining Emotion Intensity Using a Bi-directional {LSTM}-{CNN} Model",
    author = "He, Yuanye  and
      Yu, Liang-Chih  and
      Lai, K. Robert  and
      Liu, Weiyi",
    editor = "Balahur, Alexandra  and
      Mohammad, Saif M.  and
      van der Goot, Erik",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W17-5233/",
    doi = "10.18653/v1/W17-5233",
    pages = "238--242",
    abstract = "The EmoInt-2017 task aims to determine a continuous numerical value representing the intensity to which an emotion is expressed in a tweet. Compared to classification tasks that identify 1 among n emotions for a tweet, the present task can provide more fine-grained (real-valued) sentiment analysis. This paper presents a system that uses a bi-directional LSTM-CNN model to complete the competition task. Combining bi-directional LSTM and CNN, the prediction process considers both global information in a tweet and local important information. The proposed method ranked sixth among twenty-one teams in terms of Pearson Correlation Coefficient."
}