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Experiments on a Flickr dataset demonstrates that our model improves the performance on inferring users’ emotions +37.4%

How Do Your Friends On Social Media Disclose Your Emotions?

PROCEEDINGS OF THE TWENTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, pp.306-312, (2014)

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Abstract

Extracting emotions from images has attracted much interest, in particular with the rapid development of social networks. The emotional impact is very important for understanding the intrinsic meanings of images. Despite many studies having been done, most existing methods focus on image content, but ignore the emotion of the user who pub...More

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Introduction
  • People use colorful images to express their happiness, while gloomy images are used to express sadness.
  • With the rapid development of online social networks, e.g., Flickr 1 and instagram 2, more and more people like to share their daily emotional experiences using these platforms.
  • The authors' preliminary statistics indicate that more than 38% of the images on Flickr are explicitly annotated with either positive or negative emotions.
  • In online social networks such as Flickr and Instagram, posting discussions on a shared image is becoming common.
  • On Flickr, when a user publishes an image, on average 7.5 friends will leave comments (when users follow each other on Flickr, the authors say they
Highlights
  • Image is a natural way to express one’s emotions
  • To address the above challenges, we propose a novel emotion learning model to integrate both the image content and the corresponding comments
  • We propose an emotion learning method, which bridges the image and comment information by utilizing a latent space
  • Can friends’ interactions help us better understand one’s emotions? In this paper, we propose a novel emotion learning method, which models the comment information and visual features of images simultaneously by learning a latent space to bridge these two pieces of information
  • Experiments on a Flickr dataset demonstrates that our model improves the performance on inferring users’ emotions +37.4%
Methods
  • LDA+SVM EL+SVM SVM PFG LDA+SVM EL+SVM SVM PFG LDA+SVM EL+SVM SVM.
  • 0.337 0.312 0.324 Disgust PFG LDA+SVM EL+SVM Fear.
  • 0.191 0.142 0.163 Sadness F 1 F 1 −Comments −Tie
Results
  • The dataset, all codes, and visual features used in the experiments are public available 3.
Conclusion
  • The authors propose a novel emotion learning method, which models the comment information and visual features of images simultaneously by learning a latent space to bridge these two pieces of information.
  • Can friends’ interactions help them better understand one’s emotions?
  • Experiments on a Flickr dataset demonstrates that the model improves the performance on inferring users’ emotions +37.4%
Tables
  • Table1: Notations in the proposed model
  • Table2: Performance of emotion inference. Precision Recall F1-score Emotion Method
  • Table3: Image interpretations. We demonstrate how each visual feature distributes over each category of images by μet learned by the proposed model. The visual features include saturation (SR), saturation contrast (SRC), bright contrast (BRC), cool color ratio (CCR), figure-ground color difference (FGC), figure-ground area difference (FGA), background texture complexity (BTC), and foreground texture complexity (FTC). We also show a user’s friends response after the user publishes different categories of images
Download tables as Excel
Funding
  • The work is supported by the National High-tech R&D Program (No 2014AA015103), National Basic Research Program of China (No 2011CB302201, No 2014CB340500), Natural Science Foundation of China (No 61222212)
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