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Using Millions Of Emoji Occurrences To Learn Any-domain Representations For Detecting Sentiment, Emotion And Sarcasm

Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan, Sune Lehmann . Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing 2017 – 350 citations

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Affective Computing Datasets EMNLP Evaluation Interdisciplinary Approaches

NLP tasks are often limited by scarcity of manually annotated data. In social media sentiment analysis and related tasks, researchers have therefore used binarized emoticons and specific hashtags as forms of distant supervision. Our paper shows that by extending the distant supervision to a more diverse set of noisy labels, the models can learn richer representations. Through emoji prediction on a dataset of 1246 million tweets containing one of 64 common emojis we obtain state-of-the-art performance on 8 benchmark datasets within sentiment, emotion and sarcasm detection using a single pretrained model. Our analyses confirm that the diversity of our emotional labels yield a performance improvement over previous distant supervision approaches.

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