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
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 available upon request and will be made publicly
available upon acceptance of the paper.

通过 EmoTwiCS 数据集，本研究介绍了适用于 Twitter 客户服务对话的情绪轨迹自动检测，以监测客户满意度和防止负面口碑。数据集包含了 9,489 个荷兰语对话，覆盖了细粒度标记的情绪和相关事件，研究结果证明了该数据集的高质量。