Predicting customer satisfaction in customer support conversations in social media using affective features

Jonathan Herzig, Guy Feigenblat, Michal Shmueli-Scheuer, David Konopnicki, Anat Rafaeli

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Providing customer support through social media channels is gaining popularity. In such a context, predicting customer satisfaction in an early stage of a service conversation is important. Such an analysis can help personalize agent assignment to maximize customer satisfaction, and prioritize conversations. In this paper, we show that affective features such as customer's and agent's personality traits and emotion expression improve prediction of customer satisfaction when added to more typical text based features. We only utilize information extracted from the first customer conversation turn and previous customer and agent social network activity. Thus, our customer satisfaction classifier outputs its prediction in an early stage of the conversation, before any interaction has taken place between the customer and an agent. Our model was trained and tested on a Twitter conversations dataset of two customer support services, and shows an improvement of 30% in F1-score for predicting dissatisfaction.

Original languageEnglish
Title of host publicationUMAP 2016 - Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages115-119
Number of pages5
ISBN (Electronic)9781450343701
DOIs
StatePublished - 13 Jul 2016
Externally publishedYes
Event24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016 - Halifax, Canada
Duration: 13 Jul 201617 Jul 2016

Publication series

NameUMAP 2016 - Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

Conference

Conference24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016
Country/TerritoryCanada
CityHalifax
Period13/07/1617/07/16

Keywords

  • Affective computing
  • Classification
  • Customer support

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