@inproceedings{khosla-2018-emotionx,
    title = "{E}motion{X}-{AR}: {CNN}-{DCNN} autoencoder based Emotion Classifier",
    author = "Khosla, Sopan",
    editor = "Ku, Lun-Wei  and
      Li, Cheng-Te",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/W18-3507/",
    doi = "10.18653/v1/W18-3507",
    pages = "37--44",
    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."
}