TweetNLP

📄 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:

https://github.com/cardiffnlp/tweetnlp

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.

Funding:

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.