@inproceedings{rotim-etal-2017-takelab,
    title = "{T}ake{L}ab at {S}em{E}val-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news",
    author = "Rotim, Leon  and
      Tutek, Martin  and
      {\v{S}}najder, Jan",
    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/S17-2148/",
    doi = "10.18653/v1/S17-2148",
    pages = "866--871",
    abstract = "This paper describes our system for fine-grained sentiment scoring of news headlines submitted to SemEval 2017 task 5{--}subtask 2. Our system uses a feature-light method that consists of a Support Vector Regression (SVR) with various kernels and word vectors as features. Our best-performing submission scored 3rd on the task out of 29 teams and 4th out of 45 submissions with a cosine score of 0.733."
}