Online complaint handling: a text analytics-based classification framework
Marketing Intelligence & Planning
ISSN: 0263-4503
Article publication date: 28 April 2023
Issue publication date: 7 July 2023
Abstract
Purpose
This 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/approach
In 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.
Findings
This 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/value
This 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.
Keywords
Citation
Dobrucalı Yelkenci, B., Özdağoğlu, G. and İlter, B. (2023), "Online complaint handling: a text analytics-based classification framework", Marketing Intelligence & Planning, Vol. 41 No. 5, pp. 557-573. https://doi.org/10.1108/MIP-05-2022-0188
Publisher
:Emerald Publishing Limited
Copyright © 2023, Emerald Publishing Limited