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

doi:10.18653/v1/W18-6205

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 proceeding › Conference contribution › Scientific › peer-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 language	English
Title of host publication	Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Editors	Alexandra Balahur, Saif M. Mohammad, Veronique Hoste, Roman Klinger
Number of pages	7
Place of Publication	Stroudsburg
Publisher	The Association for Computational Linguistics
Publication date	31 Oct 2018
Pages	24-30
ISBN (Electronic)	978-1-948087-80-3
DOIs	https://doi.org/10.18653/v1/W18-6205
Publication status	Published - 31 Oct 2018
MoE publication type	A4 Article in conference proceedings
Event	Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis - Brussels, Belgium Duration: 31 Oct 2018 → 31 Oct 2018 Conference number: 9

Fields of Science

113 Computer and information sciences
6121 Languages
6160 Other humanities

Access to Document

10.18653/v1/W18-6205Licence: CC BY

W18-6205Final published version, 340 KBLicence: CC BY

Conference on Empirical Methods in Natural Language Processing
Öhman, E. (Attendee)
31 Oct 2018 → 4 Nov 2018
Activity: Participating in or organising an event types › Organisation and participation in conferences, workshops, courses, seminars

Cite this

Öhman, E. S., Tiedemann, J., Honkela, T. U., & Kajava, K. S. A. (2018). Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation. In A. Balahur, S. M. Mohammad, V. Hoste, & R. Klinger (Eds.), Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (pp. 24-30). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W18-6205

Öhman, Emily Sofi ; Tiedemann, Jörg ; Honkela, Timo Untamo et al. / 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. editor / Alexandra Balahur ; Saif M. Mohammad ; Veronique Hoste ; Roman Klinger. Stroudsburg : The Association for Computational Linguistics, 2018. pp. 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.",

keywords = "113 Computer and information sciences, 6121 Languages, 6160 Other humanities",

author = "{\"O}hman, {Emily Sofi} and J{\"o}rg Tiedemann and Honkela, {Timo Untamo} and Kajava, {Kaisla S A}",

year = "2018",

month = oct,

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doi = "10.18653/v1/W18-6205",

language = "English",

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editor = "Balahur, {Alexandra } and Mohammad, {Saif M. } and Hoste, {Veronique } and Klinger, {Roman }",

booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",

publisher = "The Association for Computational Linguistics",

address = "United States",

note = "Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis , WASSA ; Conference date: 31-10-2018 Through 31-10-2018",

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Öhman, ES, Tiedemann, J, Honkela, TU & Kajava, KSA 2018, Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation. in A Balahur, SM Mohammad, V Hoste & R Klinger (eds), Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. The Association for Computational Linguistics, Stroudsburg, pp. 24-30, Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis , Brussels, Belgium, 31/10/2018. https://doi.org/10.18653/v1/W18-6205

Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation. / Öhman, Emily Sofi; Tiedemann, Jörg; Honkela, Timo Untamo et al.
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. ed. / Alexandra Balahur; Saif M. Mohammad; Veronique Hoste; Roman Klinger. Stroudsburg: The Association for Computational Linguistics, 2018. p. 24-30.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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

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AU - Kajava, Kaisla S A

N1 - Conference code: 9

PY - 2018/10/31

Y1 - 2018/10/31

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.

KW - 113 Computer and information sciences

KW - 6121 Languages

KW - 6160 Other humanities

U2 - 10.18653/v1/W18-6205

DO - 10.18653/v1/W18-6205

M3 - Conference contribution

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BT - Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

A2 - Balahur, Alexandra

A2 - Mohammad, Saif M.

A2 - Hoste, Veronique

A2 - Klinger, Roman

PB - The Association for Computational Linguistics

CY - Stroudsburg

T2 - Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Y2 - 31 October 2018 through 31 October 2018

ER -

Öhman ES, Tiedemann J, Honkela TU, Kajava KSA. Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation. In Balahur A, Mohammad SM, Hoste V, Klinger R, editors, Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Stroudsburg: The Association for Computational Linguistics. 2018. p. 24-30 doi: 10.18653/v1/W18-6205

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

Abstract

Fields of Science

Access to Document

Activities

Conference on Empirical Methods in Natural Language Processing

Cite this