Abstract
Nowadays thriving image-based social networks such as Flickr and Instagram are attracting more and more people’s attention. When it comes to inferring emotions from images, previous researches mainly focus on the extraction of effective image features. However, in the context of social networks, the user’s emotional state is no longer isolated, but influenced by her friends. In this paper, we aim to infer emotions from social images leveraging influence analysis. We first explore several interesting psychological phenomena on the world’s largest image-sharing website Flickr. Then we summarize these pattern into formalized factor functions. Introducing these factors into modeling, we propose a partially-labeled factor graph model to infer the emotions of social images. The experimental results shows a 23.71% promotion compared with Naïve Bayesian method and a 21.83% promotion compared with Support Vector Machine (SVM) method under the evaluation of F1-Measure, which validates the effectiveness of our method.
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Wu, B., Jia, J., Wang, X., Yang, Y., Cai, L. (2014). Inferring Emotions from Social Images Leveraging Influence Analysis. In: Huang, H., Liu, T., Zhang, HP., Tang, J. (eds) Social Media Processing. SMP 2014. Communications in Computer and Information Science, vol 489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45558-6_13
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DOI: https://doi.org/10.1007/978-3-662-45558-6_13
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