  We exploit the Facebook reaction feature in a distant supervised fashion to
train a support vector machine classifier for emotion detection, using several
feature combinations and combining different Facebook pages. We test our models
on existing benchmarks for emotion detection and show that employing only
information that is derived completely automatically, thus without relying on
any handcrafted lexicon as it's usually done, we can achieve competitive
results. The results also show that there is large room for improvement,
especially by gearing the collection of Facebook pages, with a view to the
target domain.
