  Online social media users react to content in them based on context. Emotions
or mood play a significant part of these reactions, which has filled these
platforms with opinionated content. Different approaches and applications to
make better use of this data are continuously being developed. However, due to
the nature of the data, the variety of platforms, and dynamic online user
behavior, there are still many issues to be dealt with. It remains a challenge
to properly obtain a reliable emotional status from a user prior to posting a
comment. This work introduces a methodology that explores semi-supervised
multilingual emotion detection based on the overlap of Facebook reactions and
textual data. With the resulting emotion detection system we evaluate the
possibility of using emotions and user behavior features for the task of
sarcasm detection. More than 1 million English and Chinese comments from over
62,000 public Facebook pages posts have been collected and processed, conducted
experiments show acceptable performance metrics.
