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      Which Text-Mining Technique Would Detect Most Accurate User Frustration in Chats With Conversational Agents?

      1 , 1
      Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI)
      Human Computer Interaction Conference
      4 - 6 July 2018
      Conversational agents, chat bots, text mining, supervised learning.


            Conversational agents are getting ever more prevalent in online activities. There are many different approaches to measuring acceptance rate for such systems. In this paper, we explore the option of detecting user frustration in text-based user messages. Five text mining techniques (Decision Table Majority, Naive Bayes, Multilayer Perceptron, Sequential minimal optimisation, and K*) are compared in a supervised learning scenario using different quantifiable parameters. The comparison between these techniques shows that Sequential Minimal Optimisation is quickest and most accurate for detecting user frustration in text-based user messages.


            Author and article information

            July 2018
            July 2018
            : 1-5
            [0001]Humboldt-Universität zu Berlin Germany
            © Hinrichs et al. Published by BCS Learning and Development Ltd.Proceedings of British HCI 2018. Belfast, UK.

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            Proceedings of the 32nd International BCS Human Computer Interaction Conference
            Belfast, UK
            4 - 6 July 2018
            Electronic Workshops in Computing (eWiC)
            Human Computer Interaction Conference

            1477-9358 BCS Learning & Development

            Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2018.211
            Self URI (journal page): https://ewic.bcs.org/
            Electronic Workshops in Computing

            Applied computer science,Computer science,Security & Cryptology,Graphics & Multimedia design,General computer science,Human-computer-interaction
            Conversational agents,chat bots,text mining,supervised learning.


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