RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian

Anna Rogers, Alexey Romanov, Anna Rumshisky, Svitlana Volkova, Mikhail Gronas, Alex Gribov

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    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.
    OriginalsprogEngelsk
    TitelProceedings of the 27th International Conference on Computational Linguistics
    Antal sider9
    UdgivelsesstedSanta Fe, New Mexico, USA
    ForlagAssociation for Computational Linguistics
    Publikationsdato2018
    Sider755-763
    StatusUdgivet - 2018

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