Emotion Detection on TV Show Transcripts with Sequence-Based Convolutional Neural Networks

Sayyed M. Zahiri, Jinho D. Choi


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.

Venue / Year

Proceedings of the AAAI Workshop on Affective Content Analysis (AFFCON) / 2018


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