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Elderly Users' Interaction with Conversational Agent

Published:25 September 2019Publication History

ABSTRACT

Exploring how users behave when they interact with a conversational agent has been a popular research topic recently. However, very few studies have focused on the unique features of old people's interaction with an agent. In this paper, we report the results of interviews conducted with 19 participants, with ages comprised between 30 and 70 years. The interviews were conducted after the participants used the conversational agent, "Clova" for two weeks. During the interview, the subjects were asked about the frequently used functions and the satisfying and unsatisfying aspects of the agent. In this study, the participants with ages of 50 years or older were classified as older adults, while those under the age of 50 were classified as younger adults. Then we compared the characteristics of these two user groups by conducting a text analysis of the interview script. Our finding indicated that, older adults tended to personify the agent more by using polite words such as 'Grateful', while younger adults tended to consider it as a tool by placing more importance on its convenience; Also, older adults perceived the music function as having a high importance compared to the younger adults.

References

  1. Broadbent, E. et al. 2011. Human-Robot Interaction Research to Improve Quality of Life in Elder Care-An Approach and Issues. Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence (2011).Google ScholarGoogle Scholar
  2. Gao, Y. et al. 2018. Alexa, My Love: Analyzing Reviews of Amazon Echo. 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (2018), 372--380.Google ScholarGoogle Scholar
  3. de Graaf, M.M.A. et al. 2016. Long-term evaluation of a social robot in real homes. Interaction Studies. 17, 3 (Dec. 2016), 461--490. DOI:https://doi.org/10.1075/is.17.3.08deg.Google ScholarGoogle ScholarCross RefCross Ref
  4. Hosseinpanah, A. et al. 2018. Empathy for Everyone?: The Effect of Age When Evaluating a Virtual Agent. Proceedings of the 6th International Conference on Human-Agent Interaction (New York, NY, USA, 2018), 184--190.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Kim, J.O. et al. 2011. Automatic Classification Scheme of Opinions Written in Korean. Journal of KISS?: Databases. 38, 6 (2011), 423--428.Google ScholarGoogle Scholar
  6. Kim, S. and Kim, N. 2014. A Study on the Effect of Using Sentiment Lexicon in Opinion Classification. Journal of Intelligence and Information Systems. 20, 1 (2014), 133--148.Google ScholarGoogle ScholarCross RefCross Ref
  7. Lee, S.H. et al. 2016. Sentiment analysis on movie review through building modified sentiment dictionary by movie genre. Journal of Intelligence and Information Systems. 22, 2 (2016), 97--113.Google ScholarGoogle ScholarCross RefCross Ref
  8. Lopatovska, I. and Williams, H. 2018. Personification of the Amazon Alexa: BFF or a Mindless Companion. Proceedings of the 2018 Conference on Human Information Interaction & Retrieval (New York, NY, USA, 2018), 265--268.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Mctear, M. et al. 2016. Conversational Interfaces: Devices, Wearables, Virtual Agents, and Robots. The Conversational Interface: Talking to Smart Devices. Springer International Publishing. 283--308.Google ScholarGoogle Scholar
  10. Nam, M. et al. 2015. A Method for User Sentiment Classification using Instagram Hashtags. Journal of Korea Multimedia Society. 18, 11 (2015), 1391--1399.Google ScholarGoogle ScholarCross RefCross Ref
  11. Nasukawa, T. and Yi, J. 2003. Sentiment Analysis: Capturing Favorability Using Natural Language Processing. Proceedings of the 2Nd International Conference on Knowledge Capture (New York, NY, USA, 2003), 70--77.Google ScholarGoogle Scholar
  12. Naver Clova: 2019. https://clova.ai/ko.Google ScholarGoogle Scholar
  13. Purington, A. et al. 2017. "Alexa is My New BFF": Social Roles, User Satisfaction, and Personification of the Amazon Echo. Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (New York, NY, USA, 2017), 2853--2859.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Song, J. and Lee, S. 2011. Automatic Construction of Positive/Negative Feature-Predicate Dictionary for Polarity Classification of Product Reviews. Journal of KISS?: Software and Applications. 38, 3 (2011), 157--168.Google ScholarGoogle Scholar
  15. Turk, V. 2016. Home invasion. New Scientist. 232, 3104 (2016), 16--17. DOI:https://doi.org/https://doi.org/10.1016/S0262--4079(16)32318--1.Google ScholarGoogle ScholarCross RefCross Ref
  16. Wu, Y.-H. et al. 2014. Acceptance of an assistive robot in older adults: a mixed-method study of human-robot interaction over a 1-month period in the Living Lab setting. Clinical interventions in aging. 9, (May 2014), 801--811. DOI:https://doi.org/10.2147/CIA.S56435.Google ScholarGoogle Scholar
  17. Wu, Y.-H. et al. 2012. Designing robots for the elderly: Appearance issue and beyond. Archives of Gerontology and Geriatrics. 54, 1 (2012), 121--126. DOI:https://doi.org/https://doi.org/10.1016/j.archger.2011.02.003.Google ScholarGoogle ScholarCross RefCross Ref
  18. Yu, E. et al. 2013. Predicting the Direction of the Stock Index by Using a Domain-Specific Sentiment Dictionary. Journal of Intelligence and Information Systems. 19, 1 (2013), 95--110.Google ScholarGoogle ScholarCross RefCross Ref

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    • Published in

      cover image ACM Conferences
      HAI '19: Proceedings of the 7th International Conference on Human-Agent Interaction
      September 2019
      341 pages
      ISBN:9781450369220
      DOI:10.1145/3349537

      Copyright © 2019 Owner/Author

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 25 September 2019

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      HAI '19 Paper Acceptance Rate25of68submissions,37%Overall Acceptance Rate121of404submissions,30%

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