EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks
Muhammad Abdul-Mageed,Lyle H. Ungar +1 more
- Vol. 1, pp 718-728
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TLDR
A very large dataset for fine-grained emotions and deep learning models on it are built and a new state-of-the-art on 24 fine- grained types of emotions is achieved.Abstract:
Accurate detection of emotion from natural language has applications ranging from building emotional chatbots to better understanding individuals and their lives. However, progress on emotion detection has been hampered by the absence of large labeled datasets. In this work, we build a very large dataset for fine-grained emotions and develop deep learning models on it. We achieve a new state-of-the-art on 24 fine-grained types of emotions (with an average accuracy of 87.58%). We also extend the task beyond emotion types to model Robert Plutick’s 8 primary emotion dimensions, acquiring a superior accuracy of 95.68%.read more
Citations
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Journal ArticleDOI
Deep learning for sentiment analysis: A survey
Lei Zhang,Shuai Wang,Bing Liu +2 more
TL;DR: Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results as mentioned in this paper, which is also popularly used in sentiment analysis in recent years.
Book ChapterDOI
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text
TL;DR: Sentiment analysis is the task of automatically determining from text the attitude, emotion, or some other affectual state of the author as mentioned in this paper, which is a difficult task due to the complexity and subtlety of language use.
Proceedings ArticleDOI
GoEmotions: A Dataset of Fine-Grained Emotions
TL;DR: GoEmotions, the largest manually annotated dataset of 58k English Reddit comments, labeled for 27 emotion categories or Neutral is introduced, and the high quality of the annotations via Principal Preserved Component Analysis is demonstrated.
Journal ArticleDOI
Understanding emotions in text using deep learning and big data
Ankush Chatterjee,Umang Gupta,Manoj Kumar Chinnakotla,Radhakrishnan Srikanth,Michel Galley,Puneet Agrawal +5 more
TL;DR: A novel Deep Learning based approach to detect emotions - Happy, Sad and Angry in textual dialogues using semi-automated techniques to gather large scale training data with diverse ways of expressing emotions to train the model.
Proceedings ArticleDOI
SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text
TL;DR: The analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class.
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