citation: @inproceedings{dataset_ru_sentiment,
      title = "{R}u{S}entiment: An Enriched Sentiment Analysis Dataset for Social Media in {R}ussian",
      author = "Rogers, Anna  and
          Romanov, Alexey  and
          Rumshisky, Anna  and
          Volkova, Svitlana  and
          Gronas, Mikhail  and
          Gribov, Alex",
      booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
      month = aug,
      year = "2018",
      address = "Santa Fe, New Mexico, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/C18-1064",
      pages = "755--763",
      abstract = "This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages. RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss{'} kappa of 0.58 (3 annotations per post). To diversify the dataset, 6,950 posts were pre-selected with an active learning-style strategy. We report baseline classification results, and we also release the best-performing embeddings trained on 3.2B tokens of Russian VKontakte posts.",
  }
  