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. 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.
Anthology ID:
2020.emnlp-main.106
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1376–1382
Language:
URL:
https://aclanthology.org/2020.emnlp-main.106
DOI:
10.18653/v1/2020.emnlp-main.106
Bibkey:
Cite (ACL):
Keyang Ding, Jing Li, and Yuji Zhang. 2020. Hashtags, Emotions, and Comments: A Large-Scale Dataset to Understand Fine-Grained Social Emotions to Online Topics. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1376–1382, Online. Association for Computational Linguistics.
Cite (Informal):
Hashtags, Emotions, and Comments: A Large-Scale Dataset to Understand Fine-Grained Social Emotions to Online Topics (Ding et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.106.pdf
Video:
 https://slideslive.com/38938881