Real-time personalized content catering via viewer sentiment feedback: a QoE perspective

Multimedia content service and delivery have long been plagued by the difficulty in obtaining feedback on users' true quality of experience. Existing estimation methods do not adequately cover all relevant factors, whereas questionnaires are costly, time-consuming, and impossible to scale. In t...

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Bibliographic Details
Published inIEEE network Vol. 29; no. 6; pp. 14 - 19
Main Authors Tao, Xiaoming, Dong, Linhao, Li, Yang, Zhou, Jizhe, Ge, Ning, Lu, Jianhua
Format Journal Article
LanguageEnglish
Published New York IEEE 01.11.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Advertisements
Estimating techniques
Experiments
Facial
Feature extraction
Feedback
Learning
Multimedia
Networks
Personalized
Preferences
Quality of service
Real time
Real-time systems
Sentiment analysis
Streaming media
User feedback
Video sequences
Wireless communication
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Summary:Multimedia content service and delivery have long been plagued by the difficulty in obtaining feedback on users' true quality of experience. Existing estimation methods do not adequately cover all relevant factors, whereas questionnaires are costly, time-consuming, and impossible to scale. In this work, we present a framework for estimating a viewer's reactions toward on-screen content in real time by capturing and analyzing his/her facial video, thus allowing up-to-date learning of the viewer's preferences to occur, enabling the content provider to serve the most desirable and relevant contents and advertisements. Experiments have shown that the proposed sentiment analysis method can predict the viewer's preferences with good accuracy.
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ISSN:0890-8044
1558-156X
DOI:10.1109/MNET.2015.7340419