@inproceedings{schouten-etal-2017-commit,
    title = "{COMMIT} at {S}em{E}val-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines",
    author = "Schouten, Kim  and
      Frasincar, Flavius  and
      de Jong, Franciska",
    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/jlcl-multiple-ingestion/S17-2151/",
    doi = "10.18653/v1/S17-2151",
    pages = "883--887",
    abstract = "This paper describes our submission to Task 5 of SemEval 2017, Fine-Grained Sentiment Analysis on Financial Microblogs and News, where we limit ourselves to performing sentiment analysis on news headlines only (track 2). The approach presented in this paper uses a Support Vector Machine to do the required regression, and besides unigrams and a sentiment tool, we use various ontology-based features. To this end we created a domain ontology that models various concepts from the financial domain. This allows us to model the sentiment of actions depending on which entity they are affecting (e.g., {\textquoteleft}decreasing debt' is positive, but {\textquoteleft}decreasing profit' is negative). The presented approach yielded a cosine distance of 0.6810 on the official test data, resulting in the 12th position."
}