  Detecting what emotions are expressed in text is a well-studied problem in
natural language processing. However, research on finer grained emotion
analysis such as what causes an emotion is still in its infancy. We present
solutions that tackle both emotion recognition and emotion cause detection in a
joint fashion. Considering that common-sense knowledge plays an important role
in understanding implicitly expressed emotions and the reasons for those
emotions, we propose novel methods that combine common-sense knowledge via
adapted knowledge models with multi-task learning to perform joint emotion
classification and emotion cause tagging. We show performance improvement on
both tasks when including common-sense reasoning and a multitask framework. We
provide a thorough analysis to gain insights into model performance.
