pyviralcontent 0.1.0

A package for analyzing content readability and virality potential.

PyViralContent

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pyviralcontent is a Python package designed to assess the

readability of various types of content and predict their potential to

go viral. It employs multiple readability tests and translates numerical

scores into qualitative descriptors based on the Likert scale. This tool

is useful for optimizing content for clarity and engagement across

various domains.

Installation

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pip install pyviralcontent

Usage

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from pyviralcontent import ContentAnalyzer



# Define the type of content and the actual content

content_type = 'educational'  # options: 'scientific', 'blog', 'video', 'technical', 'fictional', 'legal', 'educational', 'news','advertising', 'social_media'.

text_content = "Your text content here."



# Create an instance of ContentAnalyzer

analyzer = ContentAnalyzer(text_content, content_type)



# Perform the analysis

df, viral_probability = analyzer.analyze()



# Print the results

print(f"Readability Scores Summary for {content_type.capitalize()} Content:")

print(df)

print(f"The probability of the content going viral is: {viral_probability * 100:.2f}%")

Features

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• Multiple readability tests for different content types.

• Qualitative descriptors based on the Likert scale.

• Estimation of content’s virality potential.

• Supported content types include: scientific, blog, video, technical,

  fictional, legal, educational, news, advertising, social_media.

Contributing

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Contributions to pyviralcontent are welcome! Please feel free to

submit issues, fork the repository, and create pull requests.

License

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This project is licensed under the MIT License - see the LICENSE file

for details.

Contact

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Bhaskar Tripathi - [e-mail] GitHub:

https://github.com/bhaskatripathi/pyviralcontent