$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

BERTopic을 활용한 인간-로봇 상호작용 동향 연구
A Study on Human-Robot Interaction Trends Using BERTopic 원문보기

지능정보연구 = Journal of intelligence and information systems, v.29 no.3, 2023년, pp.185 - 209  

김정훈 (국민대학교 비즈니스IT전문대학원 4단계 BK21 교육연구팀) ,  곽기영 (국민대학교 경영대학)

초록
AI-Helper 아이콘AI-Helper

4차 산업혁명의 도래와 함께 다양한 기술이 주목을 받고 있다. 4차 산업혁명과 관련된 기술로는 IoT(Internet of Things), 빅데이터, 인공지능, VR(Virtual Reality), 3D 프린터, 로봇공학 등이 있으며 이러한 기술은 종종 융합된다. 특히 로봇 분야는 빅데이터, 인공지능, VR, 디지털 트윈과 같은 기술과 결합할 것으로 기대된다. 이에 따라 로봇을 활용한 연구가 다수 진행되고 있으며 유통, 공항, 호텔, 레스토랑, 교통 분야 등에 적용되고 있다. 이러한 상황에서 인간-로봇 상호작용에 대한 연구가 주목을 받고 있지만 아직 만족할 만한 수준에는 이르지 못하고 있다. 하지만 완벽한 의사소통이 가능한 로봇에 대한 연구가 꾸준히 이루어지고 있고 이는 인간의 감정노동을 대신할 수 있을 것으로 기대된다. 따라서 현재의 인간-로봇 상호작용 기술을 비즈니스에 적용할 수 있는지에 대한 논의가 필요하다. 이를 위해 본 연구는 첫째, 인간로봇 상호작용 기술의 동향을 살펴본다. 둘째, LDA(Latent Dirichlet Allocation) 토픽모델링과 BERTopic 토픽모델링 방법을 비교한다. 연구 결과, 1992년~2002년 간의 연구에서는 인간-로봇 상호작용에 대한 개념과 기초적인 상호작용에 대해 논의되고 있었다. 2003년~2012년에는 사회적 표현에 대한 연구가 많이 진행되었으며 얼굴검출, 인식 등과 같이 판단과 관련된 연구도 수행되었다. 2013년~2022년에는 노인 간호, 교육, 자폐 치료와 같은 서비스 토픽들이 등장하였으며, 사회적 표현에 대한 연구가 지속되었다. 그러나 아직까지 비즈니스에 적용할 수 있는 수준에는 이르지 못한 것으로 보인다. 그리고 LDA토픽모델링과 BERTopic 토픽모델링 방법을 비교한 결과 LDA에 비해 BERTopic이 더 우수한 방법임을 확인하였다.

Abstract AI-Helper 아이콘AI-Helper

With the advent of the 4th industrial revolution, various technologies have received much attention. Technologies related to the 4th industry include the Internet of Things (IoT), big data, artificial intelligence, virtual reality (VR), 3D printers, and robotics, and these technologies are often con...

주제어

표/그림 (16)

참고문헌 (66)

  1. 강은경, 정연식, 양선욱, 권지윤, & 양성병. (2022).?MIS Quarterly 연구동향 탐색: 토픽모델링?및 키워드 네트워크 분석 활용. 지능정보연구,?28(2), 207-235. 

  2. 김민구, 김용우, 정태현, & 김영민. (2022). Organic?Light-Emitting Diodes 디스플레이 기술의?특허 동향과 기술적 가치에 관한 탐색적 연구.?지능정보연구, 28(4), 135-155. 

  3. 문길성. (2021). 단문의 주제 분석을 위한 LDA 와?BTM 토픽모형 평가. Journal of The Korean?Data Analysis Society, 23(3), 1313-1326.? 

  4. Alnusyan, R., Almotairi, R., Almufadhi, S., Shargabi,?A. A., & Alshobaili, J. (2020, September). A?semi-supervised approach for user reviews?topic modeling and classification. In 2020?International Conference on Computing and?Information Technology (ICCIT-1441) (pp. 1-5).?IEEE. 

  5. Angelov, D. (2020). Top2vec: Distributed representations?of topics. arXiv preprint arXiv:2008.09470. 

  6. Ao, Z., Horvath, G., Sheng, C., Song, Y., & Sun, Y.?(2023). Skill requirements in job advertisements:?A comparison of skill-categorization methods?based on wage regressions. Information Processing?& Management, 60(2), 103185. 

  7. Arnold, T., & Scheutz, M. (2017). The tactile?ethics of soft robotics: Designing wisely for?human-robot interaction. Soft robotics, 4(2),?81-87. 

  8. Bergamaschi, S., Po, L., & Sorrentino, S. (2014,?April). Comparing topic models for a movie?recommendation system. In International?Conference on Web Information Systems and?Technologies (Vol. 2, pp. 172-183). SciTePress. 

  9. Bethel, C. L., & Murphy, R. R. (2007). Survey of?non-facial/non-verbal affective expressions for?appearance-constrained robots. IEEE Transactions?on Systems, Man, and Cybernetics, Part C?(Applications and Reviews), 38(1), 83-92. 

  10. Blei, D. M. (2012). Probabilistic topic models.?Communications of the ACM, 55(4), 77-84. 

  11. Broadbent, E. (2017). Interactions with robots: The?truths we reveal about ourselves. Annual review?of psychology, 68, 627-652. 

  12. Burke, J. L., Murphy, R. R., Rogers, E., Lumelsky,?V. J., & Scholtz, J. (2004). Final report for?the DARPA/NSF interdisciplinary study on?human-robot interaction. IEEE Transactions?on Systems, Man, and Cybernetics, Part C?(Applications and Reviews), 34(2), 103-112. 

  13. Carros, F., Meurer, J., Loffler, D., Unbehaun, D.,?Matthies, S., Koch, I., ... & Wulf, V. (2020,?April). Exploring human-robot interaction?with the elderly: results from a ten-week case?study in a care home. In Proceedings of the?2020 CHI Conference on Human Factors in?Computing Systems (pp. 1-12). 

  14. Celuch, K. (2020). Customers' experience of?purchasing event tickets: mining online reviews?based on topic modeling and sentiment?analysis. International Journal of Event and?Festival Management, 12(1), 36-50. 

  15. Chan, J., & Nejat, G. (2012). Social intelligence?for a robot engaging people in cognitive?training activities. International Journal of?Advanced Robotic Systems, 9(4), 113. 

  16. Chandrasekaran, B., & Conrad, J. M. (2015,?April). Human-robot collaboration: A survey.?In SoutheastCon 2015 (pp. 1-8). IEEE. 

  17. Chuah, S. H. W., & Yu, J. (2021). The future of?service: The power of emotion in human-robot?interaction. Journal of Retailing and Consumer?Services, 61, 102551. 

  18. Chuah, S. H. W., & Yu, J. (2021). The future of?service: The power of emotion in human-robot?interaction. Journal of Retailing and Consumer?Services, 61, 102551. 

  19. Churchill, R., & Singh, L. (2022). The evolution?of topic modeling. ACM Computing Surveys,?54(10s), 1-35. 

  20. de Visser, E., & Parasuraman, R. (2011). Adaptive?aiding of human-robot teaming: Effects of?imperfect automation on performance, trust,?and workload. Journal of Cognitive Engineering?and Decision Making, 5(2), 209-231. 

  21. Egger, R., & Yu, J. (2022). A topic modeling?comparison between lda, nmf, top2vec, and?bertopic to demystify twitter posts. Frontiers?in sociology, 7, 886498. 

  22. Fang, H. C., Ong, S. K., & Nee, A. Y. C. (2014).?A novel augmented reality-based interface for?robot path planning. International Journal on?Interactive Design and Manufacturing (IJIDeM),?8, 33-42. 

  23. Feldman, R., & Dagan, I. (1995, August). Knowledge?Discovery in Textual Databases (KDT). In?KDD (Vol. 95, pp. 112-117). 

  24. Gallagher, R. J., Reing, K., Kale, D., & Ver Steeg, G.?(2017). Anchored correlation explanation: Topic?modeling with minimal domain knowledge.?Transactions of the Association for Computational?Linguistics, 5, 529-542. 

  25. Goldin-Meadow, S. (1999). The role of gesture in?communication and thinking. Trends in?cognitive sciences, 3(11), 419-429. 

  26. Goodrich, M. A., & Schultz, A. C. (2008). Human-robot interaction: a survey. Foundations and?Trends® in Human-Computer Interaction, 1(3),?203-275. 

  27. Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B.?(2007). Topics in semantic representation.?Psychological review, 114(2), 211. 

  28. Grootendorst, M. (2022). BERTopic: Neural topic?modeling with a class-based TF-IDF procedure.?arXiv preprint arXiv:2203.05794. 

  29. Hall, E. T. (1966). The hidden dimension (Vol.?609). Anchor. 

  30. Hancock, P. A., Billings, D. R., Schaefer, K. E.,?Chen, J. Y., De Visser, E. J., & Parasuraman,?R. (2011). A meta-analysis of factors affecting?trust in human-robot interaction. Human?factors, 53(5), 517-527. 

  31. Henschel, A., Hortensius, R., & Cross, E. S.?(2020). Social cognition in the age of human-robot interaction. Trends in Neurosciences,?43(6), 373-384. 

  32. Hentout, A., Aouache, M., Maoudj, A., & Akli, I.?(2019). Human-robot interaction in industrial?collaborative robotics: a literature review of?the decade 2008-2017. Advanced Robotics,?33(15-16), 764-799. 

  33. Hofmann, T. (1999, August). Probabilistic latent?semantic indexing. In Proceedings of the 22nd?annual international ACM SIGIR conference?on Research and development in information?retrieval (pp. 50-57). 

  34. Hofmann, T. (1999, August). Probabilistic latent?semantic indexing. In Proceedings of the 22nd?annual international ACM SIGIR conference?on Research and development in information?retrieval (pp. 50-57). 

  35. Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X.,?Li, Y., & Zhao, L. (2019). Latent Dirichlet?allocation (LDA) and topic modeling:?models, applications, a survey. Multimedia?Tools and Applications, 78, 15169-15211. 

  36. Joe, W. Y., & Song, S. Y. (2019). Applying?human-robot interaction technology in retail?industries. International Journal of Mechanical?Engineering and Robotics Research, 8(6),?839-844. 

  37. Kim, Y., & Mutlu, B. (2014). How social distance?shapes human-robot interaction. International?Journal of Human-Computer Studies, 72(12),?783-795. 

  38. Kukushkin, K., Ryabov, Y., & Borovkov, A. (2022).?Digital twins: A systematic literature review?based on data analysis and topic modeling.?Data, 7(12), 173. 

  39. Lee, J., Park, H. A., Park, S. K., & Song, T. M.?(2020). Using social media data to understand?consumers' information needs and emotions?regarding cancer: ontology-based data analysis?study. Journal of Medical Internet Research,?22(12), e18767. 

  40. Li, J., Louie, W. Y. G., Mohamed, S., Despond, F., &?Nejat, G. (2016, December). A user-study with?tangy the bingo facilitating robot and long-term?care residents. In 2016 IEEE international?symposium on robotics and intelligent sensors?(IRIS) (pp. 109-115). IEEE. 

  41. Lu, L., Cai, R., & Gursoy, D. (2019). Developing?and validating a service robot integration?willingness scale. International Journal of?Hospitality Management, 80, 36-51. 

  42. Malik, A. A., & Brem, A. (2021). Digital twins for?collaborative robots: A case study in human-robot?interaction. Robotics and Computer-Integrated?Manufacturing, 68, 102092. 

  43. Nass, C., & Moon, Y. (2000). Machines and?mindlessness: Social responses to computers.?Journal of social issues, 56(1), 81-103. 

  44. Nehaniv, C. L., Dautenhahn, K., Kubacki, J.,?Haegele, M., Parlitz, C., & Alami, R. (2005,?August). A methodological approach relating?the classification of gesture to identification of?human intent in the context of human-robot?interaction. In ROMAN 2005. IEEE International?Workshop on Robot and Human Interactive?Communication, 2005. (pp. 371-377). IEEE. 

  45. Neto, P., Simao, M., Mendes, N., & Safeea, M.?(2019). Gesture-based human-robot interaction?for human assistance in manufacturing. The?International Journal of Advanced Manufacturing?Technology, 101, 119-135. 

  46. Newman, D., Lau, J. H., Grieser, K., & Baldwin,?T. (2010, June). Automatic evaluation of topic?coherence. In Human language technologies:?The 2010 annual conference of the North?American chapter of the association for?computational linguistics (pp. 100-108). 

  47. Onnasch, L., & Roesler, E. (2021). A taxonomy to?structure and analyze human-robot interaction.?International Journal of Social Robotics, 13(4),?833-849. 

  48. Parasuraman, S., & Alutto, J. A. (1984). Sources and?outcomes of stress in organizational settings:?Toward the development of a structural model.?Academy of Management Journal, 27(2), 330-350. 

  49. Prentice, C., & Nguyen, M. (2020). Engaging and?retaining customers with AI and employee?service. Journal of Retailing and Consumer?Services, 56, 102186. 

  50. Qureshi, S. R., & Gupta, A. (2014, March). Towards?efficient big data and data analytics: a review.?In 2014 conference on IT in business, industry?and government (CSIBIG) (pp. 1-6). IEEE. 

  51. Roberts, R. D., Zeidner, M., & Matthews, G. (2007).?Emotional intelligence: Knowns and unknowns.?The science of emotional intelligence: Knowns?and unknowns, 419-474. 

  52. Likhitha, S., Harish, B. S., & Kumar, H. K. (2019). A?detailed survey on topic modeling for document?and short text data. International Journal of?Computer Applications, 178(39), 1-9. 

  53. Salem, M., Rohlfing, K., Kopp, S., & Joublin, F.?(2011, July). A friendly gesture: Investigating?the effect of multimodal robot behavior in?human-robot interaction. In 2011 ro-man (pp.?247-252). IEEE. 

  54. Sanchez-Franco, M. J., & Rey-Moreno, M. (2022). Do?travelers' reviews depend on the destination??An analysis in coastal and urban peer-to-peer?lodgings. Psychology & marketing, 39(2), 441-459. 

  55. Saunderson, S., & Nejat, G. (2019). How robots?influence humans: A survey of nonverbal?communication in social human-robot interaction.?International Journal of Social Robotics, 11,?575-608. 

  56. Sen, W., Hong, Z., & Xiaomei, Z. (2022). Effects?of human-machine interaction on employee's?learning: A contingent perspective. Frontiers?in Psychology, 13, 876933. 

  57. Shao, M., Snyder, M., Nejat, G., & Benhabib, B.?(2020). User affect elicitation with a socially?emotional robot. Robotics, 9(2), 44. 

  58. Smith, C. (2019). An employee's best friend? How?AI can boost employee engagement and?performance. Strategic HR Review, 18(1), 17-20. 

  59. Stede, M., & Patz, R. (2021, August). The climate?change debate and natural language processing.?In Proceedings of the 1st Workshop on NLP?for Positive Impact (pp. 8-18). 

  60. Tao, J., & Tan, T. (2005, October). Affective computing:?A review. In International Conference on?Affective computing and intelligent interaction?(pp. 981-995). Berlin, Heidelberg: Springer?Berlin Heidelberg. 

  61. Trevelyan, J. (1999). Redefining robotics for the?new millennium. The International Journal of?Robotics Research, 18(12), 1211-1223. 

  62. Umamaheswaran, S., Dar, V., Sharma, E., & Kurian,?J. S. (2023). Mapping Climate Themes From?2008-2021-An Analysis of Business News?Using Topic Models. IEEE Access, 11, 26554-26565. 

  63. Vichitkraivin, P., & Naenna, T. (2021). Factors?of healthcare robot adoption by medical staff?in Thai government hospitals. Health and?Technology, 11, 139-151. 

  64. Walters, M. L., Dautenhahn, K., Te Boekhorst, R.,?Koay, K. L., Kaouri, C., Woods, S., ... &?Werry, I. (2005, August). The influence of?subjects' personality traits on personal spatial?zones in a human-robot interaction experiment.?In ROMAN 2005. IEEE International Workshop?on Robot and Human Interactive Communication,?2005. (pp. 347-352). IEEE. 

  65. Zeng, Z., Chen, P. J., & Lew, A. A. (2020). From?high-touch to high-tech: COVID-19 drives?robotics adoption. Tourism geographies, 22(3),?724-734. 

  66. Zhang, T., Su, G., Qing, C., Xu, X., Cai, B., &?Xing, X. (2019). Hierarchical lifelong learning?by sharing representations and integrating?hypothesis. IEEE Transactions on Systems,?Man, and Cybernetics: Systems, 51(2), 1004-1014. 

저자의 다른 논문 :

관련 콘텐츠

이 논문과 함께 이용한 콘텐츠

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로