Multimodal emoji prediction

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• dc.contributor.author Barbieri, Francescoca
• dc.contributor.author Ballesteros, Miguelca
• dc.contributor.author Ronzano, Francescoca
• dc.contributor.author Saggion, Horacioca
• dc.date.accessioned 2018-08-28T09:16:26Z
• dc.date.available 2018-08-28T09:16:26Z
• dc.date.issued 2018
• dc.description.abstract Emojis are small images that are commonly included in social media text messages. The combination of visual and textual content in the same message builds up a modern way of communication, that automatic systems are not used to deal with. In this paper we extend recent advances in emoji prediction by putting forward a multimodal approach that is able to predict emojis in Instagram posts. Instagram posts are composed of pictures together with texts which sometimes include emojis. We show that these emojis can be predicted by using the text, but also using the picture. Our main finding is that incorporating the two synergistic modalities, in a combined model, improves accuracy in an emoji prediction task. This result demonstrates that these two modalities (text and images) encode different information on the use of emojis and therefore can complement each other.
• dc.description.sponsorship Francesco B. and Horacio S. acknowledge support from the TUNER project (TIN2015-65308-C5-5-R, MINECO/FEDER, UE) and the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
• dc.format.mimetype application/pdf
• dc.identifier.citation Barbieri F, Ballesteros M, Ronzano F, Saggion H. Multimodal emoji prediction. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2; 2018 June 1-6; New Orleans, Louisiana. Stroudsburg: Association for Computational Linguistics; 2018. p. 679-86.
• dc.identifier.isbn 978-1-948087-29-2
• dc.identifier.uri http://hdl.handle.net/10230/35397
• dc.language.iso eng
• dc.publisher ACL (Association for Computational Linguistics)ca
• dc.relation.ispartof Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2; 2018 June 1-6; New Orleans, Louisiana. Stroudsburg: Association for Computational Linguistics; 2018.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-65308-C
• dc.rights © ACL, Creative Commons Attribution 4.0 License
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.title Multimodal emoji predictionca
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion