TY  - GEN
A1  - Bin Tareaf, Raad
A1  - Berger, Philipp
A1  - Hennig, Patrick
A1  - Meinel, Christoph
T1  - ASEDS
BT  - Towards automatic social emotion detection system using facebook reactions
T2  - 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))
N2  - 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.
KW  - Emotion Mining
KW  - Psychological Emotions
KW  - Machine Learning
KW  - Social Media Analysis
KW  - Natural Language Processing
Y1  - 2018
SN  - 978-1-5386-6614-2
U6  - https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00143
SP  - 860
EP  - 866
PB  - IEEE
CY  - New York
ER  -