Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation

Emily Sofi Öhman, Jörg Tiedemann, Timo Untamo Honkela, Kaisla S A Kajava

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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 selfperpetuating 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 opensource and can easily be extended and applied for various purposes.
Original languageEnglish
Title of host publicationProceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
EditorsAlexandra Balahur, Saif M. Mohammad, Veronique Hoste, Roman Klinger
Number of pages7
Place of PublicationStroudsburg
PublisherThe Association for Computational Linguistics
Publication date31 Oct 2018
ISBN (Electronic)9781948087803
Publication statusPublished - 31 Oct 2018
MoE publication typeA4 Article in conference proceedings
EventWorkshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis - Brussels, Belgium
Duration: 31 Oct 201831 Oct 2018
Conference number: 9

Fields of Science

  • 113 Computer and information sciences
  • 6121 Languages
  • 6160 Other humanities

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