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

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

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.
Originalspråkengelska
Titel på gästpublikationProceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Antal sidor7
UtgivningsortStroudsburg
FörlagAssociation for Computational Linguistics
Utgivningsdatum31 okt 2018
Sidor24-30
ISBN (elektroniskt)9781948087803
StatusPublicerad - 31 okt 2018
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangWorkshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis - Bruseels, Belgien
Varaktighet: 31 okt 2018 → …
Konferensnummer: 9

Vetenskapsgrenar

  • 113 Data- och informationsvetenskap
  • 6121 Språkvetenskaper
  • 6160 Övriga humanistiska vetenskaper

Citera det här

Öhman, E. S., Tiedemann, J., Honkela, T. U., & Kajava, K. (2018). Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation. I Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (s. 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. s. 24-30
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title = "Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation",
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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year = "2018",
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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. i Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics, Stroudsburg, s. 24-30, Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis , Bruseels, Belgien, 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. s. 24-30.

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer 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. I Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Stroudsburg: Association for Computational Linguistics. 2018. s. 24-30