Bringing context into emoji recommendations (poster)

JG Kim, T Gong, E Huang, J Kim, SJ Lee… - Proceedings of the 17th …, 2019 - dl.acm.org
Proceedings of the 17th annual international conference on mobile systems …, 2019dl.acm.org
We present Reeboc that combines machine learning and k-means clustering to analyze the
conversation of a chat, extract different emotions or topics of the conversation, and
recommend emojis that represent various contexts to the user. Instead of simply analyzing a
single input sentence, we consider recent sentences exchanged in a conversation. we
performed a user study with 17 participants in 8 groups in a realistic mobile chat
environment. Participants spent the least amount of time in identifying and selecting the …
We present Reeboc that combines machine learning and k-means clustering to analyze the conversation of a chat, extract different emotions or topics of the conversation, and recommend emojis that represent various contexts to the user. Instead of simply analyzing a single input sentence, we consider recent sentences exchanged in a conversation. we performed a user study with 17 participants in 8 groups in a realistic mobile chat environment. Participants spent the least amount of time in identifying and selecting the emojis of their choice with Reeboc (38% faster than without emoji recommendation).
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