F. Salim, F. Haider, Owen Conlan, S. Luz, N. Campbell
{"title":"Analyzing Multimodality of Video for User Engagement Assessment","authors":"F. Salim, F. Haider, Owen Conlan, S. Luz, N. Campbell","doi":"10.1145/2818346.2820775","DOIUrl":null,"url":null,"abstract":"These days, several hours of new video content is uploaded to the internet every second. It is simply impossible for anyone to see every piece of video which could be engaging or even useful to them. Therefore it is desirable to identify videos that might be regarded as engaging automatically, for a variety of applications such as recommendation and personalized video segmentation etc. This paper explores how multimodal characteristics of video, such as prosodic, visual and paralinguistic features, can help in assessing user engagement with videos. The approach proposed in this paper achieved good accuracy (maximum F score of 96.93 %) through a novel combination of features extracted directly from video recordings, demonstrating the potential of this method in identifying engaging content.","PeriodicalId":20486,"journal":{"name":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2015 ACM on International Conference on Multimodal Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2818346.2820775","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
These days, several hours of new video content is uploaded to the internet every second. It is simply impossible for anyone to see every piece of video which could be engaging or even useful to them. Therefore it is desirable to identify videos that might be regarded as engaging automatically, for a variety of applications such as recommendation and personalized video segmentation etc. This paper explores how multimodal characteristics of video, such as prosodic, visual and paralinguistic features, can help in assessing user engagement with videos. The approach proposed in this paper achieved good accuracy (maximum F score of 96.93 %) through a novel combination of features extracted directly from video recordings, demonstrating the potential of this method in identifying engaging content.
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