Can a Humanoid Robot be part of Organizational Work Force? A User Study leveraging on Sentiment Analysis.

Hiring robots for the workplaces is a challenging task as robots have to cater to customer demands, follow organizational protocols and behave with social etiquette. In this study, we propose to have a humanoid social robot, Nadine, as a customer service agent in an open social work environment. The objective of this study is to analyze the effects of humanoid robots on customers at work environment, and see if it can handle social scenarios. We propose to evaluate these objectives through two modes, namely, survey questionnaire and customer feedback. We also propose a novel approach to analyze customer feedback data (text) using sentic computing methods. Specifically, we employ aspect extraction and sentiment analysis to analyze the data. From our framework, we detect sentiment associated to the aspects that mainly concerned the customers during their interaction. This allows us to understand customers expectations and current limitations of robots as employees.

[1]  Nadia Magnenat-Thalmann,et al.  Nadine Humanoid Social Robotics Platform , 2019, CGI.

[2]  Erik Cambria,et al.  PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis , 2019, CSoNet.

[3]  Koichi Osuka,et al.  Development of mobile inspection robot for rescue activities: MOIRA , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[4]  Ho Seok Ahn,et al.  Healthcare Robots in Homes of Rural Older Adults , 2015, ICSR.

[5]  Erik Cambria,et al.  Phonetic-Based Microtext Normalization for Twitter Sentiment Analysis , 2017, 2017 IEEE International Conference on Data Mining Workshops (ICDMW).

[6]  Erik Cambria,et al.  Aspect extraction for opinion mining with a deep convolutional neural network , 2016, Knowl. Based Syst..

[7]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[8]  A Cheong,et al.  Development of a Robotics Waiter System for the food and beverage industry , 2015 .

[9]  Maja J. Mataric,et al.  Using Socially Assistive Human–Robot Interaction to Motivate Physical Exercise for Older Adults , 2012, Proceedings of the IEEE.

[10]  Hua Xu,et al.  Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis , 2012, Expert Syst. Appl..

[11]  Henriette Cramer,et al.  Hospital robot at work: something alien or an intelligent colleague? , 2012, CSCW.

[12]  E. Cambria,et al.  Sentic Computing , 2015, Cognitive Computation.

[13]  Maria Pateraki,et al.  Comparing task-based and socially intelligent behaviour in a robot bartender , 2013, ICMI '13.

[14]  Erik Cambria,et al.  SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings , 2018, AAAI.

[15]  David Vilares,et al.  BabelSenticNet: A Commonsense Reasoning Framework for Multilingual Sentiment Analysis , 2018, 2018 IEEE Symposium Series on Computational Intelligence (SSCI).

[16]  Takayuki Kanda,et al.  Interactive Robots as Social Partners and Peer Tutors for Children: A Field Trial , 2004, Hum. Comput. Interact..

[17]  Eric Chang,et al.  Red Opal: product-feature scoring from reviews , 2007, EC '07.

[18]  Jeonghye Han,et al.  Outreach Education Utilizing Humanoid Type Agent Robots , 2015, HAI.

[19]  Robert O. Ambrose,et al.  Robonaut 2 - The first humanoid robot in space , 2011, 2011 IEEE International Conference on Robotics and Automation.

[20]  Sven Behnke,et al.  Towards a humanoid museum guide robot that interacts with multiple persons , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[21]  Giorgio Metta,et al.  Learning the skill of archery by a humanoid robot iCub , 2010, 2010 10th IEEE-RAS International Conference on Humanoid Robots.