@inproceedings{830882686b2a46c389285da2a1e99e94,
title = "Poster: Bringing context into emoji recommendations",
abstract = "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).",
author = "Kim, {Joon Gyum} and Taesik Gong and Evey Huang and Juho Kim and Lee, {Sung Ju} and Bogoan Kim and Park, {Jae Yeon} and Woojeong Kim and Kyungsik Han and Ko, {Jeong Gil}",
year = "2019",
month = jun,
day = "12",
doi = "10.1145/3307334.3328601",
language = "English",
series = "MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services",
publisher = "Association for Computing Machinery, Inc",
pages = "514--515",
booktitle = "MobiSys 2019 - Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services",
note = "17th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2019 ; Conference date: 17-06-2019 Through 21-06-2019",
}