Word-Emoji Embeddings from large scale Messaging Data reflect real-world Semantic Associations of Expressive Icons
This work addresses the need for better emoji semantics in natural language processing, but it is incremental as it applies existing embedding methods to new messaging data.
The researchers tackled the problem of understanding semantic associations of emojis by training word-emoji embeddings on a large-scale messaging dataset from Jodel, containing over 40 million sentences with 11 million annotated with emojis, and they released a dataset of 1488 emoji embeddings.
We train word-emoji embeddings on large scale messaging data obtained from the Jodel online social network. Our data set contains more than 40 million sentences, of which 11 million sentences are annotated with a subset of the Unicode 13.0 standard Emoji list. We explore semantic emoji associations contained in this embedding by analyzing associations between emojis, between emojis and text, and between text and emojis. Our investigations demonstrate anecdotally that word-emoji embeddings trained on large scale messaging data can reflect real-world semantic associations. To enable further research we release the Jodel Emoji Embedding Dataset (JEED1488) containing 1488 emojis and their embeddings along 300 dimensions.