  While there have been significant advances in detecting emotions from speech
and image recognition, emotion detection on text is still under-explored and
remained as an active research field. This paper introduces a corpus for
text-based emotion detection on multiparty dialogue as well as deep neural
models that outperform the existing approaches for document classification. We
first present a new corpus that provides annotation of seven emotions on
consecutive utterances in dialogues extracted from the show, Friends. We then
suggest four types of sequence-based convolutional neural network models with
attention that leverage the sequence information encapsulated in dialogue. Our
best model shows the accuracies of 37.9% and 54% for fine- and coarse-grained
emotions, respectively. Given the difficulty of this task, this is promising.
