Emotion-Cause analysis has attracted the attention of researchers in recent
years. However, most existing datasets are limited in size and number of
emotion categories. They often focus on extracting parts of the document that
contain the emotion cause and fail to provide more abstractive, generalizable
root cause. To bridge this gap, we introduce a large-scale dataset of emotion
causes, derived from 9.8 million cleaned tweets over 15 years. We describe our
curation process, which includes a comprehensive pipeline for data gathering,
cleaning, labeling, and validation, ensuring the dataset's reliability and
richness. We extract emotion labels and provide abstractive summarization of
the events causing emotions. The final dataset comprises over 700,000 tweets
with corresponding emotion-cause pairs spanning 48 emotion classes, validated
by human evaluators. The novelty of our dataset stems from its broad spectrum
of emotion classes and the abstractive emotion cause that facilitates the
development of an emotion-cause knowledge graph for nuanced reasoning. Our
dataset will enable the design of emotion-aware systems that account for the
diverse emotional responses of different people for the same event.

近年来，情绪原因分析引起了研究人员的关注。为了填补现有数据集在规模和情绪类别数量上的限制，并提供更具抽象性和可推广性的根本原因，我们引入了一个由 1500 万个清理后的推文构成的大规模情绪原因数据集。我们描述了我们的筛选过程，其中包括全面的数据收集、清理、标记和验证流程，以确保数据集的可靠性和丰富性。我们提取情绪标签，并提供导致情绪的事件的抽象化摘要。最终的数据集包括超过 70 万个推文和对应的情绪 - 原因对，涵盖了 48 个情绪类别，并由人工评估员进行了验证。我们数据集的新颖之处在于它涵盖了广泛的情绪类别，并提供了抽象的情绪原因，从而促进了情绪 - 原因知识图谱的细致推理。我们的数据集将使得能够考虑不同人对同一事件的多样化情绪反应的情绪感知系统的设计成为可能。