Most of TweetNLP stems from previous research projects summarised below and described in the following article:
“TweetNLP: Cutting-Edge Natural Language Processing for Social Media” (arXiv)
If you use TweetNLP in your own projects, we would ask you to cite the reference paper above and/or any of the most relevant projects below.
TweetNLP Python library:
This website is powered by TweetNLP
, a very simple Python library that enables us to use cutting-edge NLP models specialised on social media with a single line of code!
First, install the library with:
pip install tweetnlp
Then, predict the sentiment of a given sentence/tweet as simple as:
model = tweetnlp.load('sentiment')
model.sentiment("How many more days until opening day? 😩")
For more details, go to the github repo or the tutorial section
that includes more detailed Colab Notebooks with many examples.
Github repositories and references:
For more details about individual resources comprising TweetNLP, please check out the following research projects:
TweetNLP is powered by last-generation transformer-based language models. All language models used in this website are stored in the Cardiff NLP
Hugging Face hub. In particular, these are the direct link to the task-specific NLP models in Hugging Face:
Word Embedding Models:
This project was partially funded by the UKRI Future Leaders Fellowship of Jose Camacho Collados, Snap and an “Innovation for All” scheme at Cardiff University.