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TweetNLP for all the NLP enthusiasts working on Twitter! The Python library tweetnlp provides a collection of useful tools to analyze/understand tweets such as sentiment analysis, emoji prediction, and named entity recognition, powered by state-of-the-art language models specialised on Twitter.
| Date | Stars |
|---|---|
| 2026-07-24 | 392 |
| 2026-07-25 | 392 |
| 2026-07-28 | 392 |
| 2026-07-30 | 392 |
| 2026-07-31 | 393 |
| 2026-08-06 | 393 |
Today
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+1 stars this week
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Momentum
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growth rate 0.26%/day
[](https://github.com/asahi417/tweetnlp/blob/master/LICENSE)
[](https://badge.fury.io/py/tweetnlp)
[](https://pypi.python.org/pypi/tweetnlp/)
[](https://pypi.python.org/pypi/tweetnlp/)
# TweetNLP
TweetNLP for all the NLP enthusiasts working on Twitter and social media!
The python library `tweetnlp` provides a collection of useful tools to analyze/understand tweets such as sentiment analysis,
emoji prediction, and named-entity recognition, powered by state-of-the-art language modeling specialized on social media.
***News (December 2022):*** We presented a TweetNLP demo paper ("TweetNLP: Cutting-Edge Natural Language Processing for Social Media"), at EMNLP 2022. The final version can be found [here](https://aclanthology.org/2022.emnlp-demos.5/).
***TweetNLP Hugging Face page*** All the main TweetNLP models can be found [here on Hugging Face](https://huggingface.co/collections/cardiffnlp/tweetnlp-65e6f9ff5a0c4550ef7f1c70).
Resources:
- Quick Tour with Colab Notebook: [](https://colab.research.google.com/drive/104MtF9MXkDFimlJLr4SFBX0HjidLTfvp?usp=sharing)
- Play with the TweetNLP Online Demo: [link](https://tweetnlp.org/demo/)
- EMNLP 2022 paper: [link](https://arxiv.org/abs/2206.14774)
- 2nd Cardiff NLP Summer Workshop Tutorial: [](https://colab.research.google.com/drive/1whic817jQzdHl4wKI8ZzeLGtmT9Gmv1m?usp=sharing)
- 2nd Cardiff NLP Summer Workshop Tutorial (solutions): [](https://colab.research.google.com/drive/1WVqt54dkZIBInzZTXZADNj7vCTnMXimx?usp=sharing)
Table of Contents:
1. [***Load Model & Dataset***](https://github.com/cardiffnlp/tweetnlp/tree/add_training#model--dataset)
2. [***Fine-tune Model***](https://github.com/cardiffnlp/tweetnlp/tree/add_training#model-fine-tuning)
## Get Started
Install TweetNLP via pip on your console.
```shell
pip install tweetnlp
```
## Model & Dataset
In this section, you will learn how to get the models and datasets with `tweetnlp`.
The models follow [huggingface model](https://huggingface.co/) and the datasets are in the format of [huggingface datasets](https://huggingface.co/docs/datasets/load_hub).
Easy introductions of huggingface models and datasets should be found at [huggingface webpage](https://huggingface.co/), so
please check them if you are new to huggingface.
### Tweet Classification
[](https://colab.research.google.com/drive/104MtF9MXkDFimlJLr4SFBX0HjidLTfvp#scrollTo=KAZYjeskBqL4)
The classification module consists of six different tasks (Topic Classification, Sentiment Analysis, Irony Detection, Hate Speech Detection, Offensive Language Detection, Emoji Prediction, and Emotion Analysis).
In each example, the model is instantiated by `tweetnlp.load_model("task-name")`, and run the prediction by passing a text or a list of texts as argument to the corresponding function.
- ***Topic Classification***: The aim of this task is, given a tweet to assign topics related to its content. The task is formed as a supervised multi-label classification problem where each tweet is assigned one or more topics from a total of 19 available topics. The topics were carefully curated based on Twitter trends with the aim to be broad and general and consist of classes such as: arts and culture, music, or sports. Our internally-annotated dataset contains over 10K manually-labeled tweets (check the paper [here](https://arxiv.org/abs/2209.09824), or the [huggingface dataset page](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single)).
Excerpt of 36,855 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b2e5bee437839bc4, topic:natural-language-processing, topic:sentiment-analysis, readme:natural language processing