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

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

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 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
Number of pages7
Place of PublicationStroudsburg
PublisherAssociation for Computational Linguistics
Publication date31 Oct 2018
Pages24-30
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 - Bruseels, Belgium
Duration: 31 Oct 2018 → …
Conference number: 9

Fields of Science

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

Cite this

Öhman, E. S., Tiedemann, J., Honkela, T. U., & Kajava, K. (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 (pp. 24-30). Stroudsburg: Association for Computational Linguistics.
Öhman, Emily Sofi ; Tiedemann, Jörg ; Honkela, Timo Untamo ; Kajava, Kaisla. / Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation. Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Stroudsburg : Association for Computational Linguistics, 2018. pp. 24-30
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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 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.",
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author = "{\"O}hman, {Emily Sofi} and J{\"o}rg Tiedemann and Honkela, {Timo Untamo} and Kaisla Kajava",
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Öhman, ES, Tiedemann, J, Honkela, TU & Kajava, K 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. Association for Computational Linguistics, Stroudsburg, pp. 24-30, Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis , Bruseels, Belgium, 31/10/2018.

Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation. / Öhman, Emily Sofi; Tiedemann, Jörg; Honkela, Timo Untamo; Kajava, Kaisla.

Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Stroudsburg : Association for Computational Linguistics, 2018. p. 24-30.

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

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T1 - Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation

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N2 - 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.

AB - 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.

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Öhman ES, Tiedemann J, Honkela TU, Kajava K. 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. Stroudsburg: Association for Computational Linguistics. 2018. p. 24-30