  In communication, a human would recognize the emotion of an interlocutor and
respond with an appropriate emotion, such as empathy and comfort. Toward
developing a dialogue system with such a human-like ability, we propose a
method to build a dialogue corpus annotated with two kinds of emotions. We
collect dialogues from Twitter and annotate each utterance with the emotion
that a speaker put into the utterance (expressed emotion) and the emotion that
a listener felt after listening to the utterance (experienced emotion). We
built a dialogue corpus in Japanese using this method, and its statistical
analysis revealed the differences between expressed and experienced emotions.
We conducted experiments on recognition of the two kinds of emotions. The
experimental results indicated the difficulty in recognizing experienced
emotions and the effectiveness of multi-task learning of the two kinds of
emotions. We hope that the constructed corpus will facilitate the study on
emotion recognition in a dialogue and emotion-aware dialogue response
generation.
