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

Keyang Ding, Jing Li, Yuji Zhang

Computational Social Science and Social Media Short Paper

Gather-1F: Nov 17, Gather-1F: Nov 17 (02:00-04:00 UTC) [Join Gather Meeting]

You can open the pre-recorded video in a separate window.

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. 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.
NOTE: Video may display a random order of authors. Correct author list is at the top of this page.

Connected Papers in EMNLP2020

Similar Papers

Topic Modeling in Embedding Spaces
Adji Bousso Dieng, Francisco Ruiz, David Blei,
Multi-resolution Annotations for Emoji Prediction
Weicheng Ma, Ruibo Liu, Lili Wang, Soroush Vosoughi,
Unsupervised stance detection for arguments from consequences
Jonathan Kobbe, Ioana Hulpus, Heiner Stuckenschmidt,