@inproceedings{wang-etal-2017-ynudlg-semeval,
    title = "{YNUDLG} at {S}em{E}val-2017 Task 4: A {GRU}-{SVM} Model for Sentiment Classification and Quantification in {T}witter",
    author = "Wang, Ming  and
      Chu, Biao  and
      Liu, Qingxun  and
      Zhou, Xiaobing",
    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/fix-sig-urls/S17-2119/",
    doi = "10.18653/v1/S17-2119",
    pages = "713--717",
    abstract = "Sentiment analysis is one of the central issues in Natural Language Processing and has become more and more important in many fields. Typical sentiment analysis classifies the sentiment of sentences into several discrete classes (e.g.,positive or negative). In this paper we describe our deep learning system(combining GRU and SVM) to solve both two-, three- and five-tweet polarity classifications. We first trained a gated recurrent neural network using pre-trained word embeddings, then we extracted features from GRU layer and input these features into support vector machine to fulfill both the classification and quantification subtasks. The proposed approach achieved 37th, 19th, and 14rd places in subtasks A, B and C, respectively."
}