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        <title>Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation</title>
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    <abstract>This paper introduces a gamified framework for fine-grained sentiment analysis and emotion detection. We present a flexible tool, Sentimentator, that can be used for efficient annotation based on crowd sourcing and a self-perpetuating gold standard. We also present a novel dataset with multi-dimensional annotations of emotions and sentiments in movie subtitles that enables research on sentiment preservation across languages and the creation of robust multilingual emotion detection tools. The tools and datasets are public and open-source and can easily be extended and applied for various purposes.</abstract>
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%0 Conference Proceedings
%T Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation
%A Öhman, Emily
%A Kajava, Kaisla
%A Tiedemann, Jörg
%A Honkela, Timo
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y Hoste, Veronique
%Y Klinger, Roman
%S Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F ohman-etal-2018-creating
%X This paper introduces a gamified framework for fine-grained sentiment analysis and emotion detection. We present a flexible tool, Sentimentator, that can be used for efficient annotation based on crowd sourcing and a self-perpetuating gold standard. We also present a novel dataset with multi-dimensional annotations of emotions and sentiments in movie subtitles that enables research on sentiment preservation across languages and the creation of robust multilingual emotion detection tools. The tools and datasets are public and open-source and can easily be extended and applied for various purposes.
%R 10.18653/v1/W18-6205
%U https://aclanthology.org/W18-6205
%U https://doi.org/10.18653/v1/W18-6205
%P 24-30

Markdown (Informal)

[Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation](https://aclanthology.org/W18-6205) (Öhman et al., WASSA 2018)

• Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation (Öhman et al., WASSA 2018)

ACL

• Emily Öhman, Kaisla Kajava, Jörg Tiedemann, and Timo Honkela. 2018. Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 24–30, Brussels, Belgium. Association for Computational Linguistics.