TweeNLP: A Twitter Exploration Portal for Natural Language Processing

Viraj Shah, Shruti Singh, Mayank Singh


Abstract
We present TweeNLP, a one-stop portal that organizes Twitter’s natural language processing (NLP) data and builds a visualization and exploration platform. It curates 19,395 tweets (as of April 2021) from various NLP conferences and general NLP discussions. It supports multiple features such as TweetExplorer to explore tweets by topics, visualize insights from Twitter activity throughout the organization cycle of conferences, discover popular research papers and researchers. It also builds a timeline of conference and workshop submission deadlines. We envision TweeNLP to function as a collective memory unit for the NLP community by integrating the tweets pertaining to research papers with the NLPExplorer scientific literature search engine. The current system is hosted at http://nlpexplorer.org/twitter/CFP.
Anthology ID:
2021.acl-demo.32
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
Month:
August
Year:
2021
Address:
Online
Editors:
Heng Ji, Jong C. Park, Rui Xia
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
265–271
Language:
URL:
https://aclanthology.org/2021.acl-demo.32
DOI:
10.18653/v1/2021.acl-demo.32
Bibkey:
Cite (ACL):
Viraj Shah, Shruti Singh, and Mayank Singh. 2021. TweeNLP: A Twitter Exploration Portal for Natural Language Processing. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 265–271, Online. Association for Computational Linguistics.
Cite (Informal):
TweeNLP: A Twitter Exploration Portal for Natural Language Processing (Shah et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-demo.32.pdf
Video:
 https://aclanthology.org/2021.acl-demo.32.mp4