2017
EACL
EACL 2017
Are Emojis Predictable?
Abstract
AbstractEmojis are ideograms which are naturally combined with plain text to visually complement or condense the meaning of a message. Despite being widely used in social media, their underlying semantics have received little attention from a Natural Language Processing standpoint. In this paper, we investigate the relation between words and emojis, studying the novel task of predicting which emojis are evoked by text-based tweet messages. We train several models based on Long Short-Term Memory networks (LSTMs) in this task. Our experimental results show that our neural model outperforms a baseline as well as humans solving the same task, suggesting that computational models are able to better capture the underlying semantics of emojis.
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The Questioner
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Interdisciplinary Bridge
— Deep Learning and Interdisciplinary and Machine Learning and Natural Language Processing
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Keyword Pioneer
— emoji prediction
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Hot Topic Early Bird
— emoji prediction
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Deep Learning > Architectures > Transformers
Natural Language Processing > Applications > Text Classification
Interdisciplinary > Social > Social Media Analysis
Natural Language Processing > Applications > Sentiment Analysis
Natural Language Processing > Applications > Text Generation
Machine Learning > Learning Types > Classification
Deep Learning > Learning Types > Self-Supervised Learning