2018
ACL
ACL 2018
EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier
Abstract
AbstractIn 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.
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Interdisciplinary Bridge
- Artificial Intelligence and Natural Language Processing
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Learning Types > Self-Supervised Learning
Deep Learning > Architectures > Autoencoders
Natural Language Processing > Applications > Text Classification
Natural Language Processing > Applications > Sentiment Analysis
Deep Learning > Learning Types > Representation Learning