Your reactions suggest you liked the movie: automatic content rating via reaction sensing

  • Authors:

  • Xuan Bao;Songchun Fan;Alexander Varshavsky;Kevin Li;Romit Roy Choudhury

  • Affiliations:

  • Samsung Research America, San Jose, CA, USA;Duke University, Durham, NC, USA;AT&T Labs, Florham Park, USA;AT&T Labs, Florham Park, USA;Duke University, Durham, USA

  • Venue:
  • Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
  • Year:
  • 2013

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Abstract

This paper describes a system for automatically rating content - mainly movies and videos - at multiple granularities. Our key observation is that the rich set of sensors available on today's smartphones and tablets could be used to capture a wide spectrum of user reactions while users are watching movies on these devices. Examples range from acoustic signatures of laughter to detect which scenes were funny, to the stillness of the tablet indicating intense drama. Moreover, unlike in most conventional systems, these ratings need not result in just one numeric score, but could be expanded to capture the user's experience. We combine these ideas into an Android based prototype called Pulse, and test it with 11 users each of whom watched 4 to 6 movies on Samsung tablets. Encouraging results show consistent correlation between the user's actual ratings and those generated by the system. With more rigorous testing and optimization, Pulse could be a candidate for real-world adoption.