napsternxg/TwitterNER
Twitter named entity extraction for WNUT 2016 http://noisy-text.github.io/2016/ner-shared-task.html
This project helps social media analysts, market researchers, or anyone tracking public opinion to automatically identify key places, organizations, and people mentioned in tweets. You provide raw tweet text, and it highlights and labels specific words or phrases like 'Chicago' as a 'LOCATION' or 'Apple' as an 'ORGANIZATION'. This makes it easier to quickly understand what entities are being discussed in large volumes of social media data.
140 stars. No commits in the last 6 months.
Use this if you need to quickly extract and categorize specific named entities like locations, people, or organizations from short, informal social media posts.
Not ideal if you need to analyze highly structured documents, formal texts, or require entity recognition beyond what is typically found in short tweets.
Stars
140
Forks
33
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Aug 15, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/napsternxg/TwitterNER"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
chakki-works/seqeval
A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
Hironsan/anago
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.
jbesomi/texthero
Text preprocessing, representation and visualization from zero to hero.
hamelsmu/ktext
Utilities for preprocessing text for deep learning with Keras
asahi417/tner
Language model fine-tuning on NER with an easy interface and cross-domain evaluation. "T-NER: An...