@inproceedings{camacho-collados-etal-2022-tweetnlp,
title = "{T}weet{NLP}: Cutting-Edge Natural Language Processing for Social Media",
author = "Camacho-Collados, Jose and
Rezaee, Kiamehr and
Riahi, Talayeh and
Ushio, Asahi and
Loureiro, Daniel and
Antypas, Dimosthenis and
Boisson, Joanne and
Espinosa-Anke, Luis and
Liu, Fangyu and
Mart{\'i}nez-C{\'a}mara, Eugenio and
Medina, Gonzalo and
Buhrmann, Thomas and
Neves, Leonardo and
Barbieri, Francesco",
editor = "Che, Wanxiang and
Shutova, Ekaterina",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-demos.5/",
doi = "10.18653/v1/2022.emnlp-demos.5",
pages = "38--49",
abstract = "In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media. TweetNLP supports a diverse set of NLP tasks, including generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification. Task-specific systems are powered by reasonably-sized Transformer-based language models specialized on social media text (in particular, Twitter) which can be run without the need for dedicated hardware or cloud services. The main contributions of TweetNLP are: (1) an integrated Python library for a modern toolkit supporting social media analysis using our various task-specific models adapted to the social domain; (2) an interactive online demo for codeless experimentation using our models; and (3) a tutorial covering a wide variety of typical social media applications."
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<abstract>In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media. TweetNLP supports a diverse set of NLP tasks, including generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification. Task-specific systems are powered by reasonably-sized Transformer-based language models specialized on social media text (in particular, Twitter) which can be run without the need for dedicated hardware or cloud services. The main contributions of TweetNLP are: (1) an integrated Python library for a modern toolkit supporting social media analysis using our various task-specific models adapted to the social domain; (2) an interactive online demo for codeless experimentation using our models; and (3) a tutorial covering a wide variety of typical social media applications.</abstract>
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%0 Conference Proceedings
%T TweetNLP: Cutting-Edge Natural Language Processing for Social Media
%A Camacho-Collados, Jose
%A Rezaee, Kiamehr
%A Riahi, Talayeh
%A Ushio, Asahi
%A Loureiro, Daniel
%A Antypas, Dimosthenis
%A Boisson, Joanne
%A Espinosa-Anke, Luis
%A Liu, Fangyu
%A Martínez-Cámara, Eugenio
%A Medina, Gonzalo
%A Buhrmann, Thomas
%A Neves, Leonardo
%A Barbieri, Francesco
%Y Che, Wanxiang
%Y Shutova, Ekaterina
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F camacho-collados-etal-2022-tweetnlp
%X In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media. TweetNLP supports a diverse set of NLP tasks, including generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification. Task-specific systems are powered by reasonably-sized Transformer-based language models specialized on social media text (in particular, Twitter) which can be run without the need for dedicated hardware or cloud services. The main contributions of TweetNLP are: (1) an integrated Python library for a modern toolkit supporting social media analysis using our various task-specific models adapted to the social domain; (2) an interactive online demo for codeless experimentation using our models; and (3) a tutorial covering a wide variety of typical social media applications.
%R 10.18653/v1/2022.emnlp-demos.5
%U https://aclanthology.org/2022.emnlp-demos.5/
%U https://doi.org/10.18653/v1/2022.emnlp-demos.5
%P 38-49
Markdown (Informal)
[TweetNLP: Cutting-Edge Natural Language Processing for Social Media](https://aclanthology.org/2022.emnlp-demos.5/) (Camacho-Collados et al., EMNLP 2022)
ACL
- Jose Camacho-Collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa-Anke, Fangyu Liu, Eugenio Martínez-Cámara, Gonzalo Medina, Thomas Buhrmann, Leonardo Neves, and Francesco Barbieri. 2022. TweetNLP: Cutting-Edge Natural Language Processing for Social Media. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–49, Abu Dhabi, UAE. Association for Computational Linguistics.