DOI QR코드

DOI QR Code

Positioning of Smart Speakers by Applying Text Mining to Consumer Reviews: Focusing on Artificial Intelligence Factors

텍스트 마이닝을 활용한 스마트 스피커 제품의 포지셔닝: 인공지능 속성을 중심으로

  • Received : 2020.01.20
  • Accepted : 2020.03.08
  • Published : 2020.03.31

Abstract

The smart speaker includes an AI assistant function in the existing portable speaker, which enables a person to give various commands using a voice and provides various offline services associated with control of a connected device. The speed of domestic distribution is also increasing, and the functions and linked services available through smart speakers are expanding to shopping and food orders. Through text mining-based customer review analysis, there have been many proposals for identifying the impact on customer attitudes, sentiment analysis, and product evaluation of product functions and attributes. Emotional investigation has been performed by extracting words corresponding to characteristics or features from product reviews and analyzing the impact on assessment. After obtaining the topic from the review, the effect on the evaluation was analyzed. And the market competition of similar products was visualized. Also, a study was conducted to analyze the reviews of smart speaker users through text mining and to identify the main attributes, emotional sensitivity analysis, and the effects of artificial intelligence attributes on product satisfaction. The purpose of this study is to collect blog posts about the user's experiences of smart speakers released in Korea and to analyze the attitudes of customers according to their attributes. Through this, customers' attitudes can be identified and visualized by each smart speaker product, and the positioning map of the product was derived based on customer recognition of smart speaker products by collecting the information identified by each property.

Acknowledgement

Supported by : 한국연구재단, 가톨릭대학교

References

  1. 강민영 2017. "인공지능(AI) 가전제품 문제점 및 개선방안(음성인식 스피커를 중심으로)", 한국소비자원 조사보고서.
  2. 김정훈, 송영은, 진윤선, 권오병 2015. "텍스트마이닝을 통한 댓글의 공감도 및 비공감도에 영향을 미치는 댓글의 특성 연구," 한국IT서비스학회지 (14:2), pp. 159-176.
  3. 박명석, 권영진, 이상용 2018. "댓글이 음원 판매량에 미치는 차별적 영향에 관한 텍스트마이닝 분석," 지식경영연구 (19:2), pp. 91-108.
  4. 서덕성, 모경현, 박재선, 이기창, 강필성 2017. "워드 임베딩과 그래프 기반 준지도학습을 통한 한국어 어휘 감성 점수 산출," 대한산업공학회지 (43:5), pp. 330-340.
  5. 신훈철, 김종학, 박영택 2016. "카노모델(Kano Model)을 이용한 스마트 오디오 컨셉 기능의 고객만족에 관한 연구," 한국품질경영학회 (44:4), pp. 951-963.
  6. 이민석, 양석우, 이홍주 2019. "문장 분류를 위한 정보 이득 및 유사도에 따른 단어 제거와 선택적 단어 임베딩 방안," 지능정보연구 (25:4), pp. 105-122.
  7. 이진명, 정민지, 이주래, 김예은, 안치연 2019. "인공지능 스피커에 대한 소비자 인식과 수용의도: 비수용자를 중심으로," 소비자학연구 (30:2), pp. 193-213.
  8. 안정국, 김희웅 2015. "집단지성을 이용한 한글 감성어 사전 구축," 지능정보연구 (21:2), pp. 49-67.
  9. 이홍주 2018a. "A Ghost in the Shell? 고객 리뷰를 통한 스마트 스피커의 인공지능 속성이 평가에 미치는 영향 연구," 한국IT서비스학회지 (17:2), pp. 191-205.
  10. 이홍주 2018b. "헬스케어 서비스 리뷰를 활용한 서비스 품질 차원 별 중요 단어 파악 방안," 지식경영연구 (19:4), pp. 171-185.
  11. 이홍주 2019. "인공지능 속성에 대한 고객 태도 변화: AI 스피커 고객 리뷰 분석을 통한 탐색적 연구," 지식경영연구 (20:2), pp. 25-42.
  12. 컨슈머인사이트 2018. "뜨거운 AI 스피커 시장, 차가운 소비자 평가 ", 이동통신리포트, https://www.consumerinsight.co.kr/voc_view.aspx?no=2924&id=ins02_list&PageNo=1&schFlag=0
  13. Baeza-Yates, R., B. Ribeiro-Neto 2011, Modern Information Retrieval: The Concepts and Technology behind Search (2nd Edition), Addison-Wesley Professional.
  14. Berger, J., A. Humphreys, S. Ludwig, W. W. Moe, O. Netzer, D. A. Schweidel 2020. "Uniting the Tribes: Using Text for Marketing Insight," Journal of Marketing (84:1), pp. 1-25
  15. Cao, Q., W. Duan and Q. Gan 2011. "Exploring determinants of voting for the 'helpfulness' of online user reviews: A text mining approach," Decision Support Systems (50:2), pp. 511-521. https://doi.org/10.1016/j.dss.2010.11.009
  16. Chen, K., G. Kou, J. Shang and Y. Chen 2015. "Visualizing market structure through online product reviews: Integrate topic modeling, TOPSIS, and multi-dimensional scaling approaches," Electronic Commerce Research and Applications (14:1), pp. 58-74. https://doi.org/10.1016/j.elerap.2014.11.004
  17. Ghose, A., P. G. Ipeirotis and B. Li 2012. "Design Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content," Marketing Science (31:3), pp. 493-520. https://doi.org/10.1287/mksc.1110.0700
  18. Humphreys, A. and R. J. Wang 2018. "Automated Text Analysis for Consumer Research," Journal of Consumer Research (44: 6), pp. 1274-1306. https://doi.org/10.1093/jcr/ucx104
  19. Lee, T. Y. and E. T. Bradlow 2011. "Automated marketing research using online customer reviews," Journal of Marketing Research (48:5), pp. 881-894. https://doi.org/10.1509/jmkr.48.5.881
  20. Netzer, O., R. Feldman, J. Goldenberg and M. Fresko 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science (31:3), pp. 521-543. https://doi.org/10.1287/mksc.1120.0713
  21. Scott, M., and Bondi, M. 2010. Keyness in Texts. (pp. 21-42). Amsterdam, Philadelphia: John Benjamins.