Abstract
In this paper, we model emotions in EmotionLines dataset using a convolutional-deconvolutional autoencoder (CNN-DCNN) framework. We show that adding a joint reconstruction loss improves performance. Quantitative evaluation with jointly trained network, augmented with linguistic features, reports best accuracies for emotion prediction; namely joy, sadness, anger, and neutral emotion in text.- Anthology ID:
- W18-3507
- Volume:
- Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- SocialNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 37–44
- Language:
- URL:
- https://aclanthology.org/W18-3507
- DOI:
- 10.18653/v1/W18-3507
- Cite (ACL):
- Sopan Khosla. 2018. EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier. In Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media, pages 37–44, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier (Khosla, SocialNLP 2018)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/W18-3507.pdf