@inproceedings{galeshchuk-etal-2019-sentiment,
    title = "Sentiment Analysis for Multilingual Corpora",
    author = "Galeshchuk, Svitlana  and
      Qiu, Ju  and
      Jourdan, Julien",
    editor = "Erjavec, Toma{\v{z}}  and
      Marci{\'n}czuk, Micha{\l}  and
      Nakov, Preslav  and
      Piskorski, Jakub  and
      Pivovarova, Lidia  and
      {\v{S}}najder, Jan  and
      Steinberger, Josef  and
      Yangarber, Roman",
    booktitle = "Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W19-3717/",
    doi = "10.18653/v1/W19-3717",
    pages = "120--125",
    abstract = "The paper presents a generic approach to the supervised sentiment analysis of social media content in Slavic languages. The method proposes translating the documents from the original language to English with Google{'}s Neural Translation Model. The resulted texts are then converted to vectors by averaging the vectorial representation of words derived from a pre-trained Word2Vec English model. Testing the approach with several machine learning methods on Polish, Slovenian and Croatian Twitter datasets returns up to 86{\%} of classification accuracy on the out-of-sample data."
}