📄 Main reference paper:

“TweetNLP: Cutting-Edge Natural Language Processing for Social Media”. Jose Camacho-Collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, et al. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP): System Demonstrations [Link paper]

If you use TweetNLP, we would kindly ask you to cite the reference paper above.

🌐 TweetNLP Python library:


This website is powered by a very simple Python API with which you can make use of 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:

import tweetnlp
                        model = tweetnlp.load('sentiment')
                        model.sentiment("How many more days until opening day? 😩")

Many social media tasks are currently supported, including hate speech detection, question answering and named entity recognition. Datasets and model fine-tuning functionalities are also available!

For more details, check out TweetNLP’s github repo or the tutorial section including more detailed Colab Notebooks with many examples.


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.