TY - GEN
T1 - Mid Roll Advertisement Placement Using Multi Modal Emotion Analysis
AU - Rawat, Sumanu
AU - Chopra, Aman
AU - Singh, Siddhartha
AU - Sinha, Shobhit
PY - 2019/1/1
Y1 - 2019/1/1
N2 - In recent years, owing to the ever-increasing consumer base of video content over the internet, promoting business via advertising between the videos has become a powerful strategy. Mid roll ads are the video ads that are played between the content of a video being watched by the user. While a lot of research has already been done in the field of analyzing the context of the video to suggest relevant ads, little has been done in the field of effective placement of the ads so that it does not deteriorate users’ experience. In this paper, we are proposing a new model to suggest at which particular spot in a video, an advertisement should be placed such that most people will watch more of the ad. This is done using emotion, text, action, audio and video analysis of different scenes of a video under consideration.
AB - In recent years, owing to the ever-increasing consumer base of video content over the internet, promoting business via advertising between the videos has become a powerful strategy. Mid roll ads are the video ads that are played between the content of a video being watched by the user. While a lot of research has already been done in the field of analyzing the context of the video to suggest relevant ads, little has been done in the field of effective placement of the ads so that it does not deteriorate users’ experience. In this paper, we are proposing a new model to suggest at which particular spot in a video, an advertisement should be placed such that most people will watch more of the ad. This is done using emotion, text, action, audio and video analysis of different scenes of a video under consideration.
UR - http://www.scopus.com/inward/record.url?scp=85072868709&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072868709&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-30490-4_14
DO - 10.1007/978-3-030-30490-4_14
M3 - Conference contribution
AN - SCOPUS:85072868709
SN - 9783030304898
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 159
EP - 171
BT - Artificial Neural Networks and Machine Learning – ICANN 2019
A2 - Tetko, Igor V.
A2 - Karpov, Pavel
A2 - Theis, Fabian
A2 - Kurková, Vera
PB - Springer Verlag
T2 - 28th International Conference on Artificial Neural Networks, ICANN 2019
Y2 - 17 September 2019 through 19 September 2019
ER -