An Advanced Profanity Filter
empowered by Artificial Intelligence AI
• Version 1.2 Beta is dead.
• Version 2.0 beta will be available soon (like weeks).
• A fork of ObsceneClean is under development.
• The code has been cleaned up and made more readable by orders of magnitude.
• The richness, detail, and diversity of data produced by ObsceneClean has expanded exponentially.
• ObsceneClean has gone far beyond its original purpose as a mere profanity filter.
• It detects any kind of hate speech and profiles the sender.
• Vast improvements and better detection as been implemented.
WARNING: Some may find content below offensive.
Any profanity filter worth its salt will deal with false positives well! The examples below are real world examples of homonyms that could cause a profanity filter to report a false positive. ObsceneClean looks at the meaning of words to determine what is offensive and what is not.
In the UK cigarettes are commonly called fags
Pork Faggots are meatballs in the UK
They were not trying to be funny
"Bitch" is commonly used at dog shows.
A real headline
Found in every household at one time
It really exists
Bungholes are used to plug kegs
This lure is rather common
A fine company that cares about quality
Bitches are female dogs
Many pubs with this name
Gook is a common name in Korea and infrequently in Scotland
Both macaca and nigra may be offensive depending on context
Fortunately, residents renamed this Austrian village to Fugging