Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/4833
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dc.contributor.authorRathnayaka, S.J.J.
dc.contributor.authorRanathunga, C.J.
dc.contributor.authorNavarathna, R.
dc.contributor.authorKaneswaran, A.
dc.contributor.authorBalathasan, Y.
dc.date.accessioned2022-01-04T09:56:27Z
dc.date.accessioned2022-06-27T09:57:58Z-
dc.date.available2022-01-04T09:56:27Z
dc.date.available2022-06-27T09:57:58Z-
dc.date.issued2021
dc.identifier.urihttp://repo.lib.jfn.ac.lk/ujrr/handle/123456789/4833-
dc.description.abstractMovie rating is a measure of viewer’s reaction to movie performance at the box office and also a key feature to garner publicity. Movie ratings are feedback measures given by a subset of the audience voluntarily. If the degree of effect on the human mindset can be measured through real-time behavior analyzing and rated, the results can help film houses to understand the secret of generating a commercial success movie. Prediction of movie ratings is a complex problem. Viewers, producers, directors, and production houses are curious about how a given movie will perform in theatres with different customer segments. Research works have been carried out relating to movie rating prediction using social networking, blogs articles, but much less has been explored by the consumer behavioral data and attributes while watching a movie continuously and using emotions and body movement dimensions [1 – 4], [7], [12]. We created an audience footage data set and transformed it into numerical feature data representing the audience’s behavior. Prepossessing and machine learning approaches were applied to build an efficient model that can predict the movies’ popularity.en_US
dc.language.isoenen_US
dc.publisher10th International Conference on Information and Automation for Sustainability (ICIAfS)en_US
dc.subjectFace Clusteringen_US
dc.subjectFace Verificationen_US
dc.subjectClassificationen_US
dc.subjectMultilayer Perceptron (MLP)en_US
dc.subjectK-Nearest Neighbors (kNN)en_US
dc.subjectSupport Vector Machine (SVM)en_US
dc.subjectAction Unit (AU)en_US
dc.subjectOpenCVen_US
dc.subjectVLC ActiveXen_US
dc.subjectEncodingen_US
dc.titlePredicting Movie Ratings from Audience Behaviors on Movie Trailersen_US
dc.typeArticleen_US
Appears in Collections:Computer Engineering

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