@inproceedings{yang-etal-2017-deepsa,
    title = "deep{SA} at {S}em{E}val-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in {T}witter",
    author = "Yang, Tzu-Hsuan  and
      Tseng, Tzu-Hsuan  and
      Chen, Chia-Ping",
    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/ingest-emnlp/S17-2101/",
    doi = "10.18653/v1/S17-2101",
    pages = "616--620",
    abstract = "In this paper, we describe our system implementation for sentiment analysis in Twitter. This system combines two models based on deep neural networks, namely a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network, through interpolation. Distributed representation of words as vectors are input to the system, and the output is a sentiment class. The neural network models are trained exclusively with the data sets provided by the organizers of SemEval-2017 Task 4 Subtask A. Overall, this system has achieved 0.618 for the average recall rate, 0.587 for the average F1 score, and 0.618 for accuracy."
}