ABSTRACT
Images in social networks share different destinies: some are going to become popular while others are going to be completely unnoticed. In this paper we propose to use visual sentiment features together with three novel context features to predict a concise popularity score of social images. Experiments on large scale datasets show the benefits of proposed features on the performance of image popularity prediction. Exploiting state-of-the-art sentiment features, we report a qualitative analysis of which sentiments seem to be related to good or poor popularity. To the best of our knowledge, this is the first work understanding specific visual sentiments that positively or negatively influence the eventual popularity of images.
- Y. Bae and H. Lee. Sentiment analysis of Twitter audiences: Measuring the positive or negative influence of popular twitterers. JASIST, 63(12):2521--2535, 2012. Google ScholarDigital Library
- D. Borth, R. Ji, T. Chen, T. Breuel, and S.-F. Chang. Large-scale visual sentiment ontology and detectors using adjective noun pairs. In Proc. of ACM MM, 2013. Google ScholarDigital Library
- K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. In Proc. of BMVC, 2014.Google ScholarCross Ref
- G. Chatzopoulou, C. Sheng, and M. Faloutsos. A first step towards understanding popularity in YouTube. In Proc. of INFOCOM, 2010.Google ScholarCross Ref
- T. Chen, D. Borth, T. Darrell, and S.-F. Chang. DeepSentiBank: Visual sentiment concept classification with deep convolutional neural networks. arXiv:1410.8586, 2014.Google Scholar
- T. Chen, F. X. Yu, J. Chen, Y. Cui, Y.-Y. Chen, and S.-F. Chang. Object-based visual sentiment concept analysis and application. In Proc. of ACM MM, 2014. Google ScholarDigital Library
- Y.-Y. Chen, T. Chen, W. H. Hsu, H.-Y. M. Liao, and S.-F. Chang. Predicting viewer affective comments based on image content in social media. In Proc. of ICMR, 2014. Google ScholarDigital Library
- E. Diener and D. DeFour. Does television violence enhance program popularity? JPSP, 36(3):333, 1978.Google Scholar
- F. Figueiredo, J. M. Almeida, M. A. Gonçalves, and F. Benevenuto. On the dynamics of social media popularity: A YouTube case study. TOIT, 14(4):24, 2014. Google ScholarDigital Library
- J. R. Finkel, T. Grenager, and C. Manning. Incorporating non-local information into information extraction systems by Gibbs sampling. In Proc. of ACL, 2005. Google ScholarDigital Library
- M. Huiskes, B. Thomee, and M. Lew. New trends and ideas in visual concept detection: the MIR Flickr retrieval evaluation initiative. In Proc. of ACM MIR, 2010. Google ScholarDigital Library
- A. Khosla, A. Das Sarma, and R. Hamid. What makes an image popular? In Proc. of WWW, 2014. Google ScholarDigital Library
- A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Proc. of NIPS, 2012.Google ScholarDigital Library
- P. J. McParlane, Y. Moshfeghi, and J. M. Jose. Nobody comes here anymore, it's too crowded; predicting image popularity on Flickr. In Proc. of ACM ICMR, 2014. Google ScholarDigital Library
- R. Plutchik. The nature of emotions. American Scientist, 89(4):344--350, 2001.Google ScholarCross Ref
- L. C. Totti, F. A. Costa, S. Avila, E. Valle, W. Meira Jr, and V. Almeida. The impact of visual attributes on online image diffusion. In Proc. of WebSci, 2014. Google ScholarDigital Library
Index Terms
Image Popularity Prediction in Social Media Using Sentiment and Context Features
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