Analyzing the Intensity of Complaints on Social Media
Ming Fang, Shi Zong, Jing Li, Xinyu Dai, Shujian Huang, Jiajun Chen
Abstract
Complaining is a speech act that expresses a negative inconsistency between reality and human’s expectations. While prior studies mostly focus on identifying the existence or the type of complaints, in this work, we present the first study in computational linguistics of measuring the intensity of complaints from text. Analyzing complaints from such perspective is particularly useful, as complaints of certain degrees may cause severe consequences for companies or organizations. We first collect 3,103 posts about complaints in education domain from Weibo, a popular Chinese social media platform. These posts are then annotated with complaints intensity scores using Best-Worst Scaling (BWS) method. We show that complaints intensity can be accurately estimated by computational models with best mean square error achieving 0.11. Furthermore, we conduct a comprehensive linguistic analysis around complaints, including the connections between complaints and sentiment, and a cross-lingual comparison for complaints expressions used by Chinese and English speakers. We finally show that our complaints intensity scores can be incorporated for better estimating the popularity of posts on social media.- Anthology ID:
- 2022.findings-naacl.132
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2022
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1742–1754
- Language:
- URL:
- https://aclanthology.org/2022.findings-naacl.132
- DOI:
- 10.18653/v1/2022.findings-naacl.132
- Cite (ACL):
- Ming Fang, Shi Zong, Jing Li, Xinyu Dai, Shujian Huang, and Jiajun Chen. 2022. Analyzing the Intensity of Complaints on Social Media. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1742–1754, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Analyzing the Intensity of Complaints on Social Media (Fang et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.findings-naacl.132.pdf
- Code
- nlpfang/complaint_intensity