AI helps you reading Science

AI generates interpretation videos

AI extracts and analyses the key points of the paper to generate videos automatically

Go Generating

AI Traceability

AI parses the academic lineage of this thesis

Generate MRT

AI Insight

AI extracts a summary of this paper

Weibo:

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)

Cited: 87|Views144

Full Text

Other Links

dblp.uni-trier.de academic.microsoft.com

Bibtex

Weibo

Keywords

generative model

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

Code:

Data:

Insight

Full Text

PPT (Upload PPT)

Similar

Reference

Cited

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)

Reference

Abramowitz, M., and Stegun, I. 1970. Handbook of mathematical functions.
Bernardo, J. M., and Smith, A. F. 2009. Bayesian theory, volume 405. Wiley. com.
Blei, D. M.; Ng, A. Y.; and Jordan, M. I. 200Latent dirichlet allocation. JMLR 3:993–1022.
Burges, C. J. 1998. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery 2(2):121–167.
Chang, C.-C., and Lin, C.-J. 2011. LIBSVM: A library for support vector machines. ACM TIST 2:27:1–27:27.
Christophe, V., and Rime, B. 1997. Exposure to the social sharing of emotion: Emotional impact, listener responses and secondary social sharing. European Journal of Social Psychology 27(1):37–54.
Elguebaly, T., and Bouguila, N. 2011. Bayesian learning of finite generalized gaussian mixture models on images. Signal Processing 91(4):801–820.
Heinrich, G. 2005. Parameter estimation for text analysis. Web: http://www.arbylon.net/publications/text-est.pdf.
Jaakkola, T. S., and Qi, Y. 2006. Parameter expanded variational bayesian methods. In NIPS’06, 1097–1104.
Jia, J.; Wu, S.; Wang, X.; Hu, P.; Cai, L.; and Tang, J. 2012. Can we understand van gogh’s mood?: learning to infer affects from images in social networks. In ACM Multimedia 2012, 857–860.
Jordan, M. I. 1999. An introduction to variational methods for graphical models. In Machine Learning, 183–233.
Lee, P. M. 20Bayesian statistics: an introduction. John Wiley & Sons.
Moll, J.; de Oliveira-Souza, R.; Moll, F. T.; Ignacio, F. A.; Bramati, I. E.; Caparelli-Daquer, E. M.; and Eslinger, P. J. 2005. The moral affiliations of disgust: A functional mri study. Cognitive and behavioral neurology 18(1):68–78.
Murphy, K. P. 2007. Conjugate bayesian analysis of the gaussian distribution. def 1(2σ2):16.
Ou, L.-C.; Luo, M. R.; Woodcock, A.; and Wright, A. 2004. A study of colour emotion and colour preference. part i: Colour emotions for single colours. Color Research & Application 29(3):232–240.
Resnik, P., and Hardisty, E. 2010. Gibbs sampling for the uninitiated. Technical report, DTIC Document.
Rime, B. 2005. Le partage social des emotions. Presses universitaires de France.
Shin, Y., and Kim, E. Y. 2010. Affective prediction in photographic images using probabilistic affective model. In CIVR’10, 390–397.
Tang, W.; Zhuang, H.; and Tang, J. 2011. Learning to infer social ties in large networks. In ECML/PKDD (3)’11, 381– 397.
Wainwright, M., and Jordan, M. 2008. Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning 1(1-2):1–305.

Author

[0]

[0]

[0]

[0]

[0]

[0]

[0]

[0]

Your rating :

No Ratings