  We present MoodSwipe, a soft keyboard that suggests text messages given the
user-specified emotions utilizing the real dialog data. The aim of MoodSwipe is
to create a convenient user interface to enjoy the technology of emotion
classification and text suggestion, and at the same time to collect labeled
data automatically for developing more advanced technologies. While users
select the MoodSwipe keyboard, they can type as usual but sense the emotion
conveyed by their text and receive suggestions for their message as a benefit.
In MoodSwipe, the detected emotions serve as the medium for suggested texts,
where viewing the latter is the incentive to correcting the former. We conduct
several experiments to show the superiority of the emotion classification
models trained on the dialog data, and further to verify good emotion cues are
important context for text suggestion.
