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| Content Provider | ACM Digital Library |
|---|---|
| Author | Otsuka, Kazuhiro Gatica-Perez, Daniel Biel, Joan-Isaac Sanchez-Cortes, Dairazalia Yamato, Junji Kumano, Shiro |
| Abstract | Conversational social video is becoming a worldwide trend. Video communication allows a more natural interaction, when aiming to share personal news, ideas, and opinions, by transmitting both verbal content and nonverbal behavior. However, the automatic analysis of natural mood is challenging, since it is displayed in parallel via voice, face, and body. This paper presents an automatic approach to infer 11 natural mood categories in conversational social video using single and multimodal nonverbal cues extracted from video blogs (vlogs) from YouTube. The mood labels used in our work were collected via crowdsourcing. Our approach is promising for several of the studied mood categories. Our study demonstrates that although multimodal features perform better than single channel features, not always all the available channels are needed to accurately discriminate mood in videos. |
| Starting Page | 1 |
| Ending Page | 9 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781450326483 |
| DOI | 10.1145/2541831.2541864 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-12-02 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Sentiment analysis Verbal content Moods Nonverbal behavior |
| Content Type | Text |
| Resource Type | Article |
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