mit-ccc/TweebankNLP

[LREC 2022] An off-the-shelf pre-trained Tweet NLP Toolkit (NER, tokenization, lemmatization, POS tagging, dependency parsing) + Tweebank-NER dataset

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/ 100
Emerging

This toolkit helps social media analysts and researchers extract detailed information from English tweets. It takes raw tweet text and identifies entities like people, organizations, and locations (Named Entity Recognition), breaks down sentences into words and their base forms (tokenization, lemmatization), assigns parts of speech, and analyzes grammatical structure (dependency parsing). This allows for deeper content analysis of social media conversations.

106 stars. No commits in the last 6 months.

Use this if you need to perform in-depth linguistic analysis on Twitter data, such as identifying key entities or understanding grammatical relationships within tweets.

Not ideal if your primary data source is not Twitter or if you need a solution for languages other than English.

social-media-analysis twitter-data natural-language-processing text-mining content-analysis
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 9 / 25
Maturity 16 / 25
Community 12 / 25

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Stars

106

Forks

10

Language

Python

License

Apache-2.0

Last pushed

Jan 24, 2024

Commits (30d)

0

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