Abstract
PurposeThis study aims to both identify content-based and interaction-based online consumer complaint types and predict complaint types according to the complaint magnitude rooted in complainants' personality traits, emotion, Twitter usage activity, as well as complaint's sentiment polarity, and interaction rate.Design/methodology/approachIn total, 297,000 complaint tweets were collected from Twitter, featuring over 220,000 consumer profiles and over 24 million user tweets. The obtained data were analyzed via two-step machine learning approach.FindingsThis study proposes a set of content and profile features that can be employed for determining complaint types and reveals the relationship between content features, profile features and online complaint type.Originality/valueThis study proposes a novel model for identifying types of online complaints, offering a set of content and profile features that can be used for predicting complaint type, and therefore introduces a flexible approach for enhancing online complaint management.