![](/images/graphics-bg.png)
Extracting Moods from Songs and BBC Programs Based on Emotional Context
Joint Authors
Petersen, Michael Kai
Butkus, Andrius
Source
International Journal of Digital Multimedia Broadcasting
Issue
Vol. 2008, Issue 2008 (31 Dec. 2008), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2008-10-21
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Topics
Abstract EN
The increasing amounts of media becoming available in converged digital broadcast and mobile broadband networks will require intelligent interfaces capable of personalizing the selection of content.
Aiming to capture the mood in the content, we construct a semantic space based on tags, frequently used to describe emotions associated with music in the last.fm social network.
Implementing latent semantic analysis (LSA), we model the affective context of songs based on their lyrics, and apply a similar approach to extract moods from BBC synopsis descriptions of TV episodes using TV-Anytime atmosphere terms.
Based on our early results, we propose that LSA could be implemented as machinelearning method to extract emotional context and model affective user preferences.
American Psychological Association (APA)
Petersen, Michael Kai& Butkus, Andrius. 2008. Extracting Moods from Songs and BBC Programs Based on Emotional Context. International Journal of Digital Multimedia Broadcasting،Vol. 2008, no. 2008, pp.1-12.
https://search.emarefa.net/detail/BIM-460705
Modern Language Association (MLA)
Petersen, Michael Kai& Butkus, Andrius. Extracting Moods from Songs and BBC Programs Based on Emotional Context. International Journal of Digital Multimedia Broadcasting No. 2008 (2008), pp.1-12.
https://search.emarefa.net/detail/BIM-460705
American Medical Association (AMA)
Petersen, Michael Kai& Butkus, Andrius. Extracting Moods from Songs and BBC Programs Based on Emotional Context. International Journal of Digital Multimedia Broadcasting. 2008. Vol. 2008, no. 2008, pp.1-12.
https://search.emarefa.net/detail/BIM-460705
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-460705