Emotion Prediction in Movies Using Visual Features Genre Information

Fatih Aslan, Hazim Kemal Ekenel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

There are many application fields to predict the emotion in multimedia content automatically such as offering personalized media options to the users, indexing media. With the developments in deep learning, the issues have become more popular. In this study, it is aimed to predict the emotion elicited from movies by using convolutional neural network approaches from the visual based features. In addition, the effect of movie genre in emotion prediction in terms of valence and arousal score is analyzed separately by using the LIRIS-ACCEDE movie dataset. As the main contribution, the dataset is deeply analyzed according to film genres. After that, it is categorized into the training groups in a way that the same genre movies are proportionally distributed, and well-known CNN networks are utilized for regression training.

Original languageEnglish (US)
Title of host publicationUBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages569-573
Number of pages5
ISBN (Electronic)9781728139647
DOIs
StatePublished - Sep 2019
Event4th International Conference on Computer Science and Engineering, UBMK 2019 - Samsun, Turkey
Duration: Sep 11 2019Sep 15 2019

Publication series

NameUBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering

Conference

Conference4th International Conference on Computer Science and Engineering, UBMK 2019
Country/TerritoryTurkey
CitySamsun
Period9/11/199/15/19

Keywords

  • emotion estimation
  • emotion prediction
  • emotion prediction using deep learning approaches
  • emotion prediction using genre
  • emotion prediction using visual features
  • movie emotion prediction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Health Informatics

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