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Author |
Pau Rodriguez; Diego Velazquez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez |
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Title |
Personality Trait Analysis in Social Networks Based on Weakly Supervised Learning of Shared Images |
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Journal Article |
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Year |
2020 |
Publication |
Applied Sciences |
Abbreviated Journal |
APPLSCI |
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10 |
Issue |
22 |
Pages |
8170 |
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Keywords |
sentiment analysis, personality trait analysis; weakly-supervised learning; visual classification; OCEAN model; social networks |
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Abstract |
Social networks have attracted the attention of psychologists, as the behavior of users can be used to assess personality traits, and to detect sentiments and critical mental situations such as depression or suicidal tendencies. Recently, the increasing amount of image uploads to social networks has shifted the focus from text to image-based personality assessment. However, obtaining the ground-truth requires giving personality questionnaires to the users, making the process very costly and slow, and hindering research on large populations. In this paper, we demonstrate that it is possible to predict which images are most associated with each personality trait of the OCEAN personality model, without requiring ground-truth personality labels. Namely, we present a weakly supervised framework which shows that the personality scores obtained using specific images textually associated with particular personality traits are highly correlated with scores obtained using standard text-based personality questionnaires. We trained an OCEAN trait model based on Convolutional Neural Networks (CNNs), learned from 120K pictures posted with specific textual hashtags, to infer whether the personality scores from the images uploaded by users are consistent with those scores obtained from text. In order to validate our claims, we performed a personality test on a heterogeneous group of 280 human subjects, showing that our model successfully predicts which kind of image will match a person with a given level of a trait. Looking at the results, we obtained evidence that personality is not only correlated with text, but with image content too. Interestingly, different visual patterns emerged from those images most liked by persons with a particular personality trait: for instance, pictures most associated with high conscientiousness usually contained healthy food, while low conscientiousness pictures contained injuries, guns, and alcohol. These findings could pave the way to complement text-based personality questionnaires with image-based questions. |
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ISE; 600.119 |
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Admin @ si @ RVC2020b |
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3553 |
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Author |
X. Binefa; Jordi Vitria; Xavier Roca |
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Title |
Deteccion de profundidad en imagenes monoculares mediante vision activa. |
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1993 |
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Revista de Optica Pura y Aplicada |
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26 |
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3 |
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636-648 |
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OR;ISE;MV |
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BCNPCL @ bcnpcl @ BVR1993 |
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144 |
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A. Pujol; Juan J. Villanueva |
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A supervised Modification of the Hausdorff distance for visual shape classification |
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2002 |
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International Journal of Pattern Recognition and Artificial Intelligence |
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16 |
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3 |
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349-359 |
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(IF: 0.359) |
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PuV2002 |
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273 |
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Author |
V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich |
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Title |
Adaptive Correlation Filters for Pattern Recognition |
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2006 |
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Pattern Recognition and Image Analysis |
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16 |
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3 |
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425-431 |
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Pattern recognition, Correlation filters, A adaptive filters |
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Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance. |
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ISE |
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ISE @ ise @ KMA2006a |
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673 |
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Author |
A. Diplaros; N. Vlassis; Theo Gevers |
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Title |
A Spatially Constrained Generative Model and an EM Algorithm for Image Segmentation |
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2007 |
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IEEE Transactions on Neural Networks |
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18 |
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3 |
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798-808 |
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Admin @ si @ DVG2007 |
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947 |
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