EmoTwiCS: a corpus for modelling emotion trajectories in Dutch customer service dialogues on Twitter.
Due to the rise of user-generated content, social media is increasingly adopted as a channel to deliver customer service. Given the public character of online platforms, the automatic detection of emotions forms an important application in monitoring customer satisfaction and preventing negative wor...
| Publicado en: | Language Resources & Evaluation Vol. 58; no. 2; pp. 505 - 547 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Jun2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=178064692&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 178064692 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2024 vid: 58 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 178064692 10.1007/s10579-023-09700-0 ppf: 505 ppct: 42 formats: fmt: – @attributes: type: T – @attributes: type: P size: 3.2MB tig: atl: EmoTwiCS: a corpus for modelling emotion trajectories in Dutch customer service dialogues on Twitter. aug: au: Labat, Sofie Demeester, Thomas Hoste, Véronique affil: https://ror.org/00cv9y106 LT3 Language and Translation Technology Team, Department of Translation, Interpreting and Communication, Ghent University, Groot-Brittaniëlaan 45, 9000, Ghent, Belgium https://ror.org/00cv9y106 T2K Group, IDLab, Department of Information Technology, Ghent University-Imec Belgium, Technologiepark 126, 9052, Ghent, Belgium su: Dutch language Customer services Microblogs User-generated content Emotions Customer satisfaction Research questions Emotion recognition sug: subj: Dutch language Customer services Microblogs User-generated content Emotions Customer satisfaction Research questions Emotion recognition keyword: Customer service Dutch resource Emotion analysis Emotion recognition in conversations (ERC) Social media text ab: Due to the rise of user-generated content, social media is increasingly adopted as a channel to deliver customer service. Given the public character of online platforms, the automatic detection of emotions forms an important application in monitoring customer satisfaction and preventing negative word-of-mouth. This paper introduces EmoTwiCS, a corpus of 9489 Dutch customer service dialogues on Twitter that are annotated for emotion trajectories. In our business-oriented corpus, we view emotions as dynamic attributes of the customer that can change at each utterance of the conversation. The term 'emotion trajectory' refers therefore not only to the fine-grained emotions experienced by customers (annotated with 28 labels and valence-arousal-dominance scores), but also to the event happening prior to the conversation and the responses made by the human operator (both annotated with 8 categories). Inter-annotator agreement (IAA) scores on the resulting dataset are substantial and comparable with related research, underscoring its high quality. Given the interplay between the different layers of annotated information, we perform several in-depth analyses to investigate (i) static emotions in isolated tweets, (ii) dynamic emotions and their shifts in trajectory, and (iii) the role of causes and response strategies in emotion trajectories. We conclude by listing the advantages and limitations of our dataset, after which we give some suggestions on the different types of predictive modelling tasks and open research questions to which EmoTwiCS can be applied. The dataset is made publicly available at https://lt3.ugent.be/resources/emotwics. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2024. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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