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TweetNLP: Cutting-Edge Natural Language Processing for Social Media

Authors :
Camacho-Collados, Jose
Rezaee, Kiamehr
Riahi, Talayeh
Ushio, Asahi
Loureiro, Daniel
Antypas, Dimosthenis
Boisson, Joanne
Espinosa-Anke, Luis
Liu, Fangyu
Martínez-Cámara, Eugenio
Medina, Gonzalo
Buhrmann, Thomas
Neves, Leonardo
Barbieri, Francesco
Publication Year :
2022

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.<br />Comment: EMNLP 2022 Demo paper. TweetNLP: https://tweetnlp.org/

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2206.14774
Document Type :
Working Paper