Text Mining of South Korea YouTube Comments on Sidewalk Autonomous Delivery Robots

다국어 초록 (Multilingual Abstract)

Sidewalk autonomous delivery robots (SADRs) are set to commence formal operations in South Korea, allowing the general public to encounter and interact with them in public spaces. Consequently, understanding the opinions of the populace regarding SADRs has become imperative, and one viable approach is the analysis of user-generated online comments. This research involved the analysis of 6,598 comments from nine YouTube videos to uncover the perspectives of South Korean YouTube users on SADRs. The text mining was executed through word frequency analysis and LDA topic modeling. Word frequency analysis revealed that, beyond search terms like "delivery" and "robot," the most frequently occurring words included "human," "fee," "delivery rider," "good," "sidewalk," and "use." The LDA topic modeling categorizes the identified themes into three: (1) SADRs replacing traditional manual delivery; (2) SADRs' operational technology; and (3) the real-world challenges and prospects of SADRs. This study has captured the sentiments of South Korean YouTube commenters towards SADRs, encompassing their positive feedback, concerns, and insights. It provides valuable direction for companies engaged in robotic innovation. Furthermore, it underscores the significance of leveraging YouTube video comments as a textual database in the field of robotics.

참고문헌 (Reference)

1 Konijn, E. A., "YouTube As a Research Tool : Three Approaches" 16 (16): 695-701, 2013

2 Strait, M. K., "The Public’s Perception of Human like Robots: Online Social Commentary Reflects an Appearance-based Uncanny Valley, a General Fear of a ‘Technology Takeover’, and the Unabashed Sexualization of Female-gendered Robots" 2017

3 Weinberg, D., "Sharing the Sidewalk : Observing Delivery Robot Interactions with Pedestrians During a Pilot in Pittsburgh, PA" 7 (7): 53-, 2023

4 Ratajczyk, D., "Shape of the Uncanny Valley and Emotional Attitudes Toward Robots Assessed by an Analysis of Youtube Comments" 14 (14): 1787-1803, 2022

5 Xu, J., "Semantic Word Cloud Generation Based on Word Embeddings" 2016

6 Kristoffersson, I., "Scenarios for the Development of Self-driving Vehicles in Freight Transport" 2018

7 Kim, T, "Robots, Artificial Intelligence, and Service Automation(RAISA)in Hospitality : Sentiment Analysis of YouTube Streaming Data" 32 (32): 259-275, 2022

8 Lee, A., "Robots on Campus: Understanding Public Perception of Robots Using Social Media" 2020

9 Mutlu, B., "Robots in Organizations: The Role of Workflow, Social, and Environmental Factors in Human-robot Interaction" 2008

10 "Robotis"

11 Byrd, K., "Robot vs Human : Expectations, Performances and Gaps in Off-premise Restaurant Service Modes" 33 (33): 3996-4016, 2021

12 Jung, H., "Research Trends in Text Mining : Semantic Network and Main Path Analysis of Selected Journals" 162 : 113851-, 2020

13 Kunze, O., "Replicators, Ground Drones and Crowd Logistics a Vision of Urban Logistics in the Year 2030" 19 : 286-299, 2016

14 Vlachos, E., "Public Perception of Android Robots: Indications from an Analysis of Youtube Comments" 2018

15 Miner, G., "Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications" Academic Press 2012

16 Simoni, M. D., "Optimization and Analysis of a Robot-assisted Last Mile Delivery System" 142 : 102049-, 2020

17 "Neubility"

18 Econovill, "Mobin Raised by Hyundai Motor, Delivery of Self-driving Robots is 'a Lot"

19 Jelodar, H., "Latent Dirichlet Allocation (LDA) and Topic Modeling: Models, Applications, a Survey" 78 : 15169-15211, 2019

20 Blei, D. M., "Latent Dirichlet Allocation" 3 : 993-1022, 2003

21 Chosun News, "Koreans' Unstoppable Love for YouTube…43 Million People Watch 30 Hours a Month"

22 Tongsang News, "K-Robot, Humans and the Dream of Robot the Renaissance"

23 Nlp.stanford.edu, "Introduction to Information Retrieval, Dropping Common Terms: Stop Words"

24 Cha, M., "I Tube, You Yube, Everybody Tubes: Analyzing the World’s Largest User Generated Content Video System" 2007

25 Stefan, S., "How Useful are Your Comments? Analyzing and Predicting Youtube Comments and Comment Ratings" 2010

26 LG, "Homework to Overcome for the Era of Delivery Robots"

27 Bilro, R. G., "Exploring Online Customer Engagement with Hospitality Products and Its Relationship with Involvement, Emotional States, Experience and Brand Advocacy" 28 (28): 147-171, 2019

28 Lee, M. K., "Deriving Pain Point of Last Mile Delivery Robot Based on Opinion Mining Analysis" 2023

29 "Baemin"

30 Srinivas, S., "Autonomous Robot-driven Deliveries: A Review of Recent Developments and Future Directions" 165 : 102834-, 2022

31 Arntz, E. M., "Assessment of Readiness of a Traffic Environment for Autonomous Delivery Robots" 4 : 1102302-, 2023

32 Dreisbach, C., "A Systematic Review of Natural Language Processing and Text Mining of Symptoms from Electronic Patient-authored Text Data" 125 : 37-46, 2019

33 Sievert, C., "A Method for Visualizing and Interpreting Topics" 2014

34 Platum, "41.83 Million 'YouTube' Users in September 2022, 81% of South Korea"