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.
|Title of host publication||SIGDIAL 2016 - 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||10|
|State||Published - 2016|
|Event||17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2016 - , United States|
Duration: 13 Sep 2016 → 15 Sep 2016
|Name||SIGDIAL 2016 - 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference|
|Conference||17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2016|
|Period||13/09/16 → 15/09/16|
Bibliographical notePublisher Copyright:
© 2016 Association for Computational Linguistics.