@misc{BinTareafBergerHennigetal.2018,
  author    = {Bin Tareaf, Raad and Berger, Philipp and Hennig, Patrick and Meinel, Christoph},
  title     = {ASEDS},
  series = {IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS))},
  journal   = {IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS))},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-5386-6614-2},
  doi       = {10.1109/HPCC/SmartCity/DSS.2018.00143},
  pages     = {860 -- 866},
  year      = {2018},
  abstract  = {The Massive adoption of social media has provided new ways for individuals to express their opinion and emotion online. In 2016, Facebook introduced a new reactions feature that allows users to express their psychological emotions regarding published contents using so-called Facebook reactions. In this paper, a framework for predicting the distribution of Facebook post reactions is presented. For this purpose, we collected an enormous amount of Facebook posts associated with their reactions labels using the proposed scalable Facebook crawler. The training process utilizes 3 million labeled posts for more than 64,000 unique Facebook pages from diverse categories. The evaluation on standard benchmarks using the proposed features shows promising results compared to previous research. The final model is able to predict the reaction distribution on Facebook posts with a recall score of 0.90 for "Joy" emotion.},
  language  = {en}
}