Classifying Emotions in Customer Support Dialogues in Social Media

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

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

30 Scopus citations

Abstract

Providing customer support through social media channels is gaining increasing popularity. In such a context, automatic detection and analysis of the emotions expressed by customers is important, as is identification of the emotional techniques (e.g., apology, empathy, etc.) in the responses of customer service agents. Result of such an analysis can help assess the quality of such a service, help and inform agents about desirable responses, and help develop automated service agents for social media interactions. In this paper, we show that, in addition to text based turn features, dialogue features can significantly improve detection of emotions in social media customer service dialogues and help predict emotional techniques used by customer service agents.

Original languageEnglish
Title of host publicationSIGDIAL 2016 - 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages64-73
Number of pages10
ISBN (Electronic)9781945626234
StatePublished - 2016
Externally publishedYes
Event17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2016 - , United States
Duration: 13 Sep 201615 Sep 2016

Publication series

NameSIGDIAL 2016 - 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference

Conference

Conference17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2016
Country/TerritoryUnited States
Period13/09/1615/09/16

Bibliographical note

Publisher Copyright:
© 2016 Association for Computational Linguistics.

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