Hashtags, Emotions, and Comments: A Large-Scale Dataset to Understand Fine-Grained Social Emotions to Online Topics

Keyang Ding, Jing Li, Yuji Zhang

Abstract:

This paper studies social emotions to online
discussion topics. While most prior work focus on emotions from writers, we investigate
readers’ responses and explore the public feelings to an online topic. A large-scale dataset is
collected from Chinese microblog Sina Weibo
with over 13 thousand trending topics, emotion votes in 24 fine-grained types from massive participants, and user comments to allow context understanding.1
In experiments,
we examine baseline performance to predict
a topic’s possible social emotions in a multilabel classification setting. The results show
that a seq2seq model with user comment modeling performs the best, even surpassing human prediction. More analyses shed light on
the effects of emotion types, topic description
lengths, contexts from user comments, and the
limited capacity of the existing models.