Asent: Fast, flexible and transparent sentiment analysis

Inspired by Vader, made using SpaCy, transparent from the start.

Simple Example

The following shows a simple example of how you can quickly apply sentiment analysis using asent. For more on using asent see the usage guides.

import spacy
import asent

# load spacy pipeline
nlp = spacy.load("da_core_news_lg")

# add the rule-based sentiment model
nlp.add_pipe("asent_da_v1")

# try an example
text = "jeg er ikke længere sur. 👿"
doc = nlp(text)

# visualize the results
asent.visualize(doc)

📖 Documentation

Documentation
🔧 [Installation]	Guides and instructions on how to use asent and its features.
📚 Usage Guides	Guides and instructions on how to use asent and its features.
📰 News and changelog	New additions, changes and version history.
🎛 [Documentations]	The detailed reference for augmenty's API. Including function documentation

💬 Where to ask questions

Type
🚨 FAQ	FAQ
🚨 Bug Reports	GitHub Issue Tracker
🎁 Feature Requests & Ideas	GitHub Issue Tracker
👩‍💻 Usage Questions	GitHub Discussions
🗯 General Discussion	GitHub Discussions

🎓 Citing this work

If you use this library in your research, please cite it using:

@inproceedings{asent2021,
  title={Asent: Fast, flexible and transparent sentiment analysis},
  author={Kenneth Enevoldsen},
  year={2021}
}