Analyzing the Intensity of Complaints on Social Media

TL;DR

This paper introduces the first computational approach to measure complaint intensity on social media, using a Chinese dataset and linguistic analysis, with implications for understanding social media dynamics and company impacts.

Contribution

It presents the first dataset and model for estimating complaint intensity from Chinese social media posts, along with linguistic insights and cross-lingual comparisons.

Findings

Complaint intensity can be estimated with a mean square error of 0.11.

Complaints are closely linked to sentiment and vary across languages.

Incorporating complaint scores improves social media popularity estimation.

Abstract

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.