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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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Abstract:
Comunicació presentada a la Seventh Joint Conference on Lexical and Computational Semantics, celebrada els dies 5 i 6 de juny de 2018 a Nova Orleans, EUA. ; In this paper we analyze the use of emojis in social media with respect to gender and skin tone. By gathering a dataset of over twenty two million tweets from United States some findings are clearly highlighted after performing a simple frequency-based analysis. Moreover, we carry out a semantic analysis on the usage of emojis and their modifiers (e.g. gender and skin tone) by embedding all words, emojis and modifiers into the same vector space. Our analyses reveal that some stereotypes related to the skin color and gender seem to be reflected on the use of these modifiers. For example, emojis representing hand gestures are more widely utilized with lighter skin tones, and the usage across skin tones differs significantly. At the same time, the vector corresponding to the male modifier tends to be semantically close to emojis related to business or technology, whereas their female counterparts appear closer to emojis about love or makeup. ; Francesco B. acknowledges 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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Keyword:
Tractament del llenguatge natural (Informàtica)
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URL: http://hdl.handle.net/10230/34971
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Interpretable emoji prediction via label-wise attention LSTMs
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Towards the understanding of gaming audiences by modeling Twitch emotes
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