Anbang Xu
ABSTRACT
Users are rapidly turning to social media to request and receive customer service; however, a majority of these requests were not addressed timely or even not addressed at all. To overcome the problem, we create a new conversational system to automatically generate responses for users requests on social media. Our system is integrated with state-of-the-art deep learning techniques and is trained by nearly 1M Twitter conversations between users and agents from over 60 brands. The evaluation reveals that over 40% of the requests are emotional, and the system is about as good as human agents in showing empathy to help users cope with emotional situations. Results also show our system outperforms information retrieval system based on both human judgments and an automatic evaluation metric.
- Natalya N. Bazarova, Yoon Hyung Choi, Victoria Schwanda Sosik, Dan Cosley, and Janis Whitlock. Social Sharing of Emotions on Facebook: Channel Differences, Satisfaction, and Replies. In Proc. of CSCW, 2015, 154--164. Google Scholar
Digital Library
- Joan-Isaac Biel, Oya Aran, and Daniel Gatica-Perez. You Are Known by How You Vlog: Personality Impressions and Nonverbal Behavior in Youtube. In Proc. of ICWSM, 2011, 446--449.Google Scholar
- Keith S. Coulter, Johanna Gummerus, Veronica Liljander, Emil Weman, and Minna Pihlström. Customer Engagement in a Facebook Brand Community. Management Research Review, 2012, 35, 9, 857--877. Google ScholarCross Ref
- Ethan Fast, Binbin Chen, and Michael S. Bernstein. Empath: Understanding Topic Signals in Large-Scale Text. In Proc. of CHI, 2016, 4647--4657. Google ScholarDigital Library
- F. Maxwell Harper, Daniel Moy, and Joseph A. Konstan. Facts or Friends?: Distinguishing Informational and Conversational Questions in Social Q&A Sites. In Proc. of CHI, 2009, 759--768.Google Scholar
- F. Maxwell Harper, Daphne Raban, Sheizaf Rafaeli, and Joseph A. Konstan. Predictors of Answer Quality in Online Q&A Sites. In Proc. of CHI, 2008, 865--874.Google Scholar
- Anjuli Kannan, Karol Kurach, Sujith Ravi, Tobias Kaufmann, Andrew Tomkins, Balint Miklos, Greg Corrado, Laszlo Lukacs, Marina Ganea, Peter Young, and Vivek Ramavajjala. Smart Reply: Automated Response Suggestion for Email. In Proc. of KDD, 2016, 955--964. Google ScholarDigital Library
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. Distributed Representations of Words and Phrases and Their Compositionality. In Proc. of NIPS, 2013, 3111--3119.Google Scholar
- Keith B. Murray. A Test of Services Marketing Theory: Consumer Information Acquisition Activities. The Journal of Marketing, 1991, 10--25. Google ScholarCross Ref
- NIELSEN. State of the Media: Social Media Report. 2011.Google Scholar
- Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: A Method for Automatic Evaluation of Machine Translation. In Proc. of ACL, 2002, 311--318.Google Scholar
- James W. Pennebaker, Martha E. Francis, and Roger J. Booth. Linguistic Inquiry and Word Count: Liwc 2001. Mahway: Lawrence Erlbaum Associates, 2001, 71, 2001.Google Scholar
- Tiziano Piccardi, Gregorio Convertino, Massimo Zancanaro, Ji Wang, and Cedric Archambeau. Towards Crowd-Based Customer Service: A Mixed-Initiative Tool for Managing Q&a Sites. In Proc. of CHI, 2014, 2725--2734. Google ScholarDigital Library
- Leyland F. Pitt, Richard T. Watson, and C. Bruce Kavan. Service Quality: A Measure of Information Systems Effectiveness. Management Information Systems Quarterly, 1995, 173--187. Google ScholarDigital Library
- Alan Ritter, Colin Cherry, and William B Dolan. Data-Driven Response Generation in Social Media. In Proc. of EMNLP, 2011, 583--593.Google Scholar
- Anselm L. Strauss Qualitative Analysis for Social Scientists. Cambridge University Press, 1987.Google Scholar
- Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. Sequence to Sequence Learning with Neural Networks. In Proc. of NIPS, 2014, 3104--3112.Google Scholar
- Anbang Xu, Haibin Liu, Liang Gou, Rama Akkiraju, Jalal Mahmud, Vibha Sinha, Yuheng Hu, and Mu Qiao. Predicting Perceived Brand Personality with Social Media. In Proc. of ICWSM, 2016, 436--445.Google Scholar
- http://blog.hubspot.com/marketing/twitter-response-time-data.Google Scholar
- http://lucene.apache.org.Google Scholar
Index Terms
- A New Chatbot for Customer Service on Social Media
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