2022/04/20 by Ming Fang, Fang, Ming, Shi Zong +9
Computer Science · #Computation and Language (cs.CL) #Digital Communication and Language #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Sentiment Analysis and Opinion Mining #cs.CL
paper · pdf · doi:10.48550/arxiv.2204.09366
NAACL 2022 (Findings)
arxiv created 2022/04/20 · openalex publication_date 2022/04/20 · arxiv updated 2022/04/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Complaining is a speech act that expresses a negative inconsistency between reality and human 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 create the first Chinese dataset containing 3,103 posts about complaints 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 the 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.