Socket
Socket
Sign inDemoInstall

asent

Package Overview
Dependencies
Maintainers
1
Alerts
File Explorer

Advanced tools

Socket logo

Install Socket

Detect and block malicious and high-risk dependencies

Install

asent

A python package for flexible and transparent sentiment analysis.


Maintainers
1

Asent: Fast, flexible and transparent sentiment analysis

PyPI version python version Code style: black github actions pytest github actions docs pip downloads

Asent is a rule-based sentiment analysis library for Python made using SpaCy. It is inspired by Vader, but uses a more modular ruleset, that allows the user to change e.g. the method for finding negations. Furthermore, it includes visualizers to visualize model predictions, making the model easily interpretable.

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

# create spacy pipeline
nlp = spacy.blank('en')
nlp.add_pipe('sentencizer')

# 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)

# print polarity of document, scaled to be between -1, and 1
print(doc._.polarity)
# neg=0.0 neu=0.631 pos=0.369 compound=0.7526

Naturally, a simple score can be quite unsatisfying, thus Asent implements a series of visualizer to interpret the results:

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

If we want to know why the model comes the result it does we can use the analysis style:

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

Where the value in the parenthesis (2.7) indicates the human-rating of the word, while the value outside the parenthesis indicates the value accounting for the negation. Asent also accounts for contrastive conjugations (e.g. but), casing, emoji's and punctuations. For more on how the model works check out the [usage guide].

📖 Documentation

Documentation
🔧 InstallationInstallation instructions for Asent
📚 Usage GuidesGuides and instructions on how to use asent and its features. It also gives short introduction to how the models works.
📰 News and changelogNew additions, changes and version history.
🎛 DocumentationThe detailed reference for Asents's API. Including function documentation

💬 Where to ask questions

Type
🚨 FAQFAQ
🚨 Bug ReportsGitHub Issue Tracker
🎁 Feature Requests & IdeasGitHub Issue Tracker
👩‍💻 Usage QuestionsGitHub Discussions
🗯 General DiscussionGitHub Discussions

Keywords

FAQs


Did you know?

Socket

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts

SocketSocket SOC 2 Logo

Product

  • Package Alerts
  • Integrations
  • Docs
  • Pricing
  • FAQ
  • Roadmap
  • Changelog

Packages

npm

Stay in touch

Get open source security insights delivered straight into your inbox.


  • Terms
  • Privacy
  • Security

Made with ⚡️ by Socket Inc