Parse-and-Prettify: Enhance Your AI Web Blogs with Sentiment-Based Emoji Citations
Parse-and-prettify is an innovative Python-based tool that transforms the way we reference content in AI web blogs. Utilizing the power of the Natural Language Toolkit (NLTK) for sentiment analysis and Beautiful Soup for HTML parsing, this script assigns emojis to citations based on the emotional tone of the referenced content. If the sentiment is neutral or undetectable, it cleverly selects emojis related to keywords found near the citation. In the absence of strong sentiment or keywords, parse-and-prettify livens up your articles by inserting a random emoji. Each emoji serves as an interactive link, opening the source content in a new window, providing readers with a visually engaging and intuitive way to access references. Embrace a unique and playful approach to citations with parse-and-prettify, and make your AI web blogs stand out!
Tutorial
Access the Python script for parse-and-prettify in the main.py file of this GitHub repo.
Under # Sample HTML content, view how the text for your article should be prepared and then swap out the text to your own article.
Feel free to edit the script however you need to in order to meet your needs! I mostly use it to glance at the emojis it creates and then ctrl-C and ctrl-V replace out the [1], [2], [3] references in my articles with their respective emojis.
I've also added the emoji libraries I sorted separately into keyword categories if some prefer to just use that instead of the script and code something from scratch.
Thanks for checking out parse-and-prettify!
Sincerely,
Hoppy Cat