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Image Popularity Prediction in Social Media Using Sentiment and Context Features

Published:13 October 2015Publication History

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.

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  1. Image Popularity Prediction in Social Media Using Sentiment and Context Features

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    • Published in

      cover image ACM Conferences
      MM '15: Proceedings of the 23rd ACM international conference on Multimedia
      October 2015
      1402 pages
      ISBN:9781450334594
      DOI:10.1145/2733373

      Copyright © 2015 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 13 October 2015

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      MM '15 Paper Acceptance Rate56of252submissions,22%Overall Acceptance Rate995of4,171submissions,24%

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