  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.
