@inproceedings{zhang-etal-2017-ynu-hpcc,
    title = "{YNU}-{HPCC} at {E}mo{I}nt-2017: Using a {CNN}-{LSTM} Model for Sentiment Intensity Prediction",
    author = "Zhang, You  and
      Yuan, Hang  and
      Wang, Jin  and
      Zhang, Xuejie",
    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/iwcs-25-ingestion/W17-5227/",
    doi = "10.18653/v1/W17-5227",
    pages = "200--204",
    abstract = "In this paper, we present a system that uses a convolutional neural network with long short-term memory (CNN-LSTM) model to complete the task. The CNN-LSTM model has two combined parts: CNN extracts local n-gram features within tweets and LSTM composes the features to capture long-distance dependency across tweets. Additionally, we used other three models (CNN, LSTM, BiLSTM) as baseline algorithms. Our introduced model showed good performance in the experimental results."
}