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

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http://hdl.handle.net/10138/263700

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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 . 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

Title: Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation
Author: Öhman, Emily Sofi; Tiedemann, Jörg; Honkela, Timo Untamo; Kajava, Kaisla S A
Other contributor: Balahur, Alexandra
Mohammad, Saif M.
Hoste, Veronique
Klinger, Roman
Contributor organization: Department of Digital Humanities
Language Technology
Faculty of Arts
Publisher: The Association for Computational Linguistics
Date: 2018-10-31
Language: eng
Number of pages: 7
Belongs to series: Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
ISBN: 9781948087803
DOI: https://doi.org/10.18653/v1/W18-6205
URI: http://hdl.handle.net/10138/263700
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.
Subject: 113 Computer and information sciences
6121 Languages
6160 Other humanities
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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