@inproceedings{zhang-etal-2017-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val 2017 Task 4: Using A Multi-Channel {CNN}-{LSTM} Model for Sentiment Classification",
    author = "Zhang, Haowei  and
      Wang, Jin  and
      Zhang, Jixian  and
      Zhang, Xuejie",
    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/S17-2134/",
    doi = "10.18653/v1/S17-2134",
    pages = "796--801",
    abstract = "In this paper, we propose a multi-channel convolutional neural network-long short-term memory (CNN-LSTM) model that consists of two parts: multi-channel CNN and LSTM to analyze the sentiments of short English messages from Twitter. Un-like a conventional CNN, the proposed model applies a multi-channel strategy that uses several filters of different length to extract active local n-gram features in different scales. This information is then sequentially composed using LSTM. By combining both CNN and LSTM, we can consider both local information within tweets and long-distance dependency across tweets in the classification process. Officially released results show that our system outperforms the baseline algo-rithm."
}