TY - GEN
T1 - Understanding emotions in SNS images from posters' perspectives
AU - Song, Junho
AU - Han, Kyungsik
AU - Lee, Dongwon
AU - Kim, Sang Wook
N1 - Funding Information:
ACKNOWLEDGMENTS This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (NRF-2017M3C4A7069440, NRF-2017M3C4A7083529, No.2018R1A5A7059549). REFERENCES
Publisher Copyright:
© 2020 ACM.
PY - 2020/3/30
Y1 - 2020/3/30
N2 - As the popularity of media-based social networking services (SNS), such as Instagram and Snapchat, has increased significantly, a growing body of research has analyzed SNS images in relation to emotional analysis and classification model development. However, these prior studies were based on relatively small amounts of data, where the emotions of images were labeled from viewers' perspectives, not posters' perspectives. Consequently, we analyze 120K images that reflect poster's emotion. We develop color- and content-based classification models by considering: (1) the dynamics of SNS, in terms of the volume and variety of images shared, and (2) the fact that people express their emotions through colors and objects. We demonstrate the comparable performance of our model with models proposed in prior studies and discuss the applications.
AB - As the popularity of media-based social networking services (SNS), such as Instagram and Snapchat, has increased significantly, a growing body of research has analyzed SNS images in relation to emotional analysis and classification model development. However, these prior studies were based on relatively small amounts of data, where the emotions of images were labeled from viewers' perspectives, not posters' perspectives. Consequently, we analyze 120K images that reflect poster's emotion. We develop color- and content-based classification models by considering: (1) the dynamics of SNS, in terms of the volume and variety of images shared, and (2) the fact that people express their emotions through colors and objects. We demonstrate the comparable performance of our model with models proposed in prior studies and discuss the applications.
UR - http://www.scopus.com/inward/record.url?scp=85083028767&partnerID=8YFLogxK
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U2 - 10.1145/3341105.3373923
DO - 10.1145/3341105.3373923
M3 - Conference contribution
AN - SCOPUS:85083028767
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 450
EP - 457
BT - 35th Annual ACM Symposium on Applied Computing, SAC 2020
PB - Association for Computing Machinery
T2 - 35th Annual ACM Symposium on Applied Computing, SAC 2020
Y2 - 30 March 2020 through 3 April 2020
ER -