We continue the streak of exciting September talks. This time it’s DTU alum (now MIT) Bjarke Felbo who recently caused international press frenzy (see MIT Technology Review, BBC, Newsweek, Business Insider, The Telegraph, The Register, Huffington Post (FR), Numerama (FR) for details) with his sarcasm-savvy deep learning algorithm. Now there’s a great opportunity you can get all the technical details and ask questions, etc.
- Place: Technical University of Denmark, Building 210, room 112.
- Date: September 13th, 2017
- Time: 13:00
Title: Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
Abstract: 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.