Asent: Fast, flexible and transparent sentiment analysis

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

Installation

Installing Asent is simple using pip:

pip install asent

There is no reason to update from GitHub as the version on pypi should always be the same of on GitHub.

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("en_core_web_lg")

# add the rule-based sentiment model
nlp.add_pipe("asent_en_v1")

# try an example
text = "I am not very happy, but I am also not especially sad"
doc = nlp(text)

# visualize model prediction
asent.visualize(doc, style="prediction")

If we want to know even more

# visualize the analysis performed by the model:
asent.visualize(doc[:5], style="analysis")

📖 Documentation

Documentation
🔧 Installation	Installation instructions for Asent
📚 Usage Guides	Guides and instructions on how to use asent and its features.
📰 News and changelog	New additions, changes and version history.
🎛 Documentation	The detailed reference for Asents'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}
}