@inproceedings{kar-etal-2017-ritual,
    title = "{R}i{TUAL}-{UH} at {S}em{E}val-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks",
    author = "Kar, Sudipta  and
      Maharjan, Suraj  and
      Solorio, Thamar",
    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/S17-2150/",
    doi = "10.18653/v1/S17-2150",
    pages = "877--882",
    abstract = "In this paper, we present our systems for the ``SemEval-2017 Task-5 on Fine-Grained Sentiment Analysis on Financial Microblogs and News''. In our system, we combined hand-engineered lexical, sentiment and metadata features, the representations learned from Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU) with Attention model applied on top. With this architecture we obtained weighted cosine similarity scores of 0.72 and 0.74 for subtask-1 and subtask-2, respectively. Using the official scoring system, our system ranked the second place for subtask-2 and eighth place for the subtask-1. It ranked first for both of the subtasks by the scores achieved by an alternate scoring system."
}