Security News
38% of CISOs Fear They’re Not Moving Fast Enough on AI
CISOs are racing to adopt AI for cybersecurity, but hurdles in budgets and governance may leave some falling behind in the fight against cyber threats.
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.
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.
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 | |
---|---|
🔧 Installation | Installation instructions for Asent |
📚 Usage Guides | Guides and instructions on how to use asent and its features. It also gives short introduction to how the models works. |
📰 News and changelog | New additions, changes and version history. |
🎛 Documentation | The detailed reference for Asents's API. Including function documentation |
Type | |
---|---|
🚨 FAQ | FAQ |
🚨 Bug Reports | GitHub Issue Tracker |
🎁 Feature Requests & Ideas | GitHub Issue Tracker |
👩💻 Usage Questions | GitHub Discussions |
🗯 General Discussion | GitHub Discussions |
FAQs
A python package for flexible and transparent sentiment analysis.
We found that asent demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
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.
Security News
CISOs are racing to adopt AI for cybersecurity, but hurdles in budgets and governance may leave some falling behind in the fight against cyber threats.
Research
Security News
Socket researchers uncovered a backdoored typosquat of BoltDB in the Go ecosystem, exploiting Go Module Proxy caching to persist undetected for years.
Security News
Company News
Socket is joining TC54 to help develop standards for software supply chain security, contributing to the evolution of SBOMs, CycloneDX, and Package URL specifications.