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            <title>Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier
%A Khosla, Sopan
%Y Ku, Lun-Wei
%Y Li, Cheng-Te
%S Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F khosla-2018-emotionx
%X 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.
%R 10.18653/v1/W18-3507
%U https://aclanthology.org/W18-3507
%U https://doi.org/10.18653/v1/W18-3507
%P 37-44

Markdown (Informal)

[EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier](https://aclanthology.org/W18-3507) (Khosla, SocialNLP 2018)

• EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier (Khosla, SocialNLP 2018)

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.