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XLM-T: A Multilingual Language Model Toolkit for Twitter ...
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Learning Cross-Lingual Word Embeddings from Twitter via Distant Supervision
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In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 14 (2020): Fourteenth International AAAI Conference on Web and Social Media; 72-82 ; 2334-0770 ; 2162-3449 (2020)
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Learning cross-lingual word embeddings from Twitter via distant supervision
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How Gender and Skin Tone Modifiers Affect Emoji Semantics in Twitter
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Overview of the Evalita 2018 Italian Emoji Prediction (ITAmoji) Task
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Sentiment Polarity Classification at EVALITA: Lessons Learned and Open Challenges
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Overview of the Evalita 2016 SENTIment POLarity Classification Task
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In: Proceedings of Third Italian Conference on Computational Linguistics (CLiC-it 2016) & Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2016) ; https://hal.inria.fr/hal-01414731 ; Proceedings of Third Italian Conference on Computational Linguistics (CLiC-it 2016) & Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2016), Dec 2016, Naples, Italy (2016)
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Overview of the Evalita 2016 Sentiment Polarity Classification Task
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Is This Tweet Satirical? A Computational Approach for Satire Detection in Spanish ; ¿Es satírico este tweet? Un método automático para la identificación del lenguaje satírico en español
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How gender and skin tone modifiers affect emoji semantics in Twitter
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How gender and skin tone modifiers affect emoji semantics in Twitter
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Interpretable emoji prediction via label-wise attention LSTMs
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Multimodal emoji prediction
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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. ; 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).
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URL: http://hdl.handle.net/10230/35397
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Towards the understanding of gaming audiences by modeling Twitch emotes
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