Dataset with 70200 pins from 117 users in Pinterest. Each pin contains a short raw text and an image. The images are processed using a pretrained Convolutional Neural Network and each one is transformed into a vector of 4096 features. This dataset was used in the paper "User Identification in Pinterest Through the Refinement of a Cascade Fusion of Text and Images" to idenfity specific users given their comments. The paper is publishe in the Research in Computing Science Journal, as part of the LKE 2017 conference. The dataset includes the splits used in the paper. There are nine files. text_test, text_train and text_val, contain the raw text of each pin in the corresponding split of the data. imag_test, imag_train and imag_val contain the image features of each pin in the corresponding split of the data. train_user and val_test_users contain the index of the user of each pin (between 0 and 116). There is a correspondance one-to-one among the test, train and validation files for images, text and users. There are 400 pins per user in the train set, and 100 pins per user in the validation and test sets each one. There is an additional file images.rar, that contains the raw original images, split per user. Nevertheles, the images do not maintain the indexed order of the pins in the other data files. If you have questions regarding the data, write to: jc dot gomez at ugto dot mx Cite this dataset as: Gomez, J. C., Ibarra-Manzano, M. A., & Almanza-Ojeda, D. L. (2017). User Identification in Pinterest Through the Refinement of Cascade Fusion of Text and Images. Research in Computing Science, 144, 41-52.