EmoTwiCS: A Corpus for Modelling Emotion Trajectories in Dutch Customer Service Dialogues on Twitter
Sofie Labat, Thomas Demeester, V\'eronique Hoste

TL;DR
EmoTwiCS is a comprehensive Dutch Twitter corpus of customer service dialogues annotated for emotion trajectories, enabling advanced analysis and modeling of emotional dynamics in online customer interactions.
Contribution
This paper introduces EmoTwiCS, a novel annotated corpus capturing dynamic emotions and responses in Dutch customer service Twitter conversations, with high inter-annotator agreement.
Findings
High-quality annotations with substantial inter-annotator agreement
Analysis of static and dynamic emotion patterns in dialogues
Insights into causes and response strategies affecting emotions
Abstract
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. 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…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Complex Network Analysis Techniques · Digital Communication and Language
Methodstravel james
