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

Sayyed M. Zahiri; Jinho D. Choi

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arXiv · 1708.04299

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

Sayyed M. Zahiri·Jinho D. Choi

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Abstract

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.

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BibTeX RIS

Sayyed M. Zahiri, Jinho D. Choi. 2017-08-14. Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks. https://arxiv.org/abs/1708.04299

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