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 language | English |
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| Title of host publication | UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 569-573 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781728139647 |
| DOIs | |
| Publication status | Published - Sept 2019 |
| Event | 4th International Conference on Computer Science and Engineering, UBMK 2019 - Samsun, Turkey Duration: 11 Sept 2019 → 15 Sept 2019 |
Publication series
| Name | UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering |
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Conference
| Conference | 4th International Conference on Computer Science and Engineering, UBMK 2019 |
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| Country/Territory | Turkey |
| City | Samsun |
| Period | 11/09/19 → 15/09/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- emotion estimation
- emotion prediction
- emotion prediction using deep learning approaches
- emotion prediction using genre
- emotion prediction using visual features
- movie emotion prediction