Using customers' online reviews to identify and classify human robot interaction failures in domestic robots

Shanee Honig, Alon Bartal, Tal Oron-Gilad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Little information is available regarding which types of failures robots experience in domestic settings. To further the community's knowledge, we manually classified 3062 customer reviews of robotic vacuum cleaners on Amazon.com. The resulting database was analyzed and used to create a Natural Language Processing (NLP) model capable of predicting whether a review contains a description of a failure or not. The current work describes the initial analysis and model development process as well as preliminary findings.

Original languageEnglish
Title of host publicationHRI 2020 - Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Pages251-253
Number of pages3
ISBN (Electronic)9781450370578
DOIs
StatePublished - 23 Mar 2020
Event15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020 - Cambridge, United Kingdom
Duration: 23 Mar 202026 Mar 2020

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
ISSN (Electronic)2167-2148

Conference

Conference15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020
Country/TerritoryUnited Kingdom
CityCambridge
Period23/03/2026/03/20

Keywords

  • Human-robot interaction
  • Social network analysis
  • User satisfaction
  • User-centered

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Using customers' online reviews to identify and classify human robot interaction failures in domestic robots'. Together they form a unique fingerprint.

Cite this