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Feeling is believing : viewing movies through emotional arcs

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dc.contributor.advisor Deb Roy. en_US
dc.contributor.author Chu, Eric, S.M. Massachusetts Institute of Technology en_US
dc.contributor.other Program in Media Arts and Sciences (Massachusetts Institute of Technology) en_US
dc.date.accessioned 2017-12-20T18:16:59Z
dc.date.available 2017-12-20T18:16:59Z
dc.date.copyright 2017 en_US
dc.date.issued 2017 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/112910
dc.description Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (pages 121-126). en_US
dc.description.abstract This thesis uses machine learning methods to construct emotional arcs in movies, calculate families of arcs, and demonstrate the ability for certain arcs to predict audience engagement. The system is applied to Hollywood films and high quality shorts found on the web. We begin by harnessing deep convolutional neural networks for audio and visual sentiment analysis. These models are trained on both new and existing large-scale datasets, after which they can be used to compute separate audio and visual emotional arcs for any video. We then crowd source annotations for 30-second video clips extracted from highs and lows in the arcs in order to assess the micro-level precision of the system. Precision is measured in terms of agreement in polarity between the system's predictions and annotators' ratings. The final model combining audio and visual features achieves a precision of 0.894. Next, we look at macro-level characterizations of movies by investigating whether there exist 'universal shapes' of emotional arcs. In particular, we develop a clustering approach to discover distinct classes of emotional arcs. Finally, we show on a sample corpus of short web videos that certain emotional arcs are statistically significant predictors of the number of comments a video receives. These results suggest that the emotional arcs learned by our approach successfully represent macroscopic aspects of a video story that drive audience engagement. Such machine understanding could be used to predict audience reactions to video stories, ultimately improving our ability as storytellers to communicate with each other. en_US
dc.description.statementofresponsibility by Eric Chu. en_US
dc.format.extent 126 pages en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Program in Media Arts and Sciences () en_US
dc.title Feeling is believing : viewing movies through emotional arcs en_US
dc.title.alternative Viewing movies through emotional arcs en_US
dc.type Thesis en_US
dc.description.degree S.M. en_US
dc.contributor.department Program in Media Arts and Sciences (Massachusetts Institute of Technology) en_US
dc.identifier.oclc 1015239862 en_US


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