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 these 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 9,489 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.... Mehr ...

Verfasser: Labat, Sofie
Demeester, Thomas
Hoste, Veronique
Dokumenttyp: journalarticle
Erscheinungsdatum: 2024
Schlagwörter: Languages and Literatures / Technology and Engineering / Business and Economics / LT3 / emotion analysis / emotion recognition in conversations (ERC) / customer service / social media text / Dutch resource
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-29033084
Datenquelle: BASE; Originalkatalog
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Link(s) : https://biblio.ugent.be/publication/01HCM6T355HJ6F1WMJH060A62D