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.
@article{arxiv.1708.04299,
title = {Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks},
author = {Sayyed M. Zahiri and Jinho D. Choi},
journal= {arXiv preprint arXiv:1708.04299},
year = {2017}
}