Alright folks, lemme tell ya ’bout this little project I tackled. The goal? Censor some… let’s call ’em “spicy” words in a wrestling-themed app I’m tinkering with. Think WWE, but without the PG-13 violations, ya know?
First thing I did was compile a list. A BIG list. Searched all over the internet, dredged up some old forum threads, even had a buddy help me out. Ended up with a text file packed with every swear word, slur, and general term of unpleasantness I could find. Pretty nasty stuff, honestly, but gotta know the enemy, right?
Then, I fired up my trusty text editor. I’m a Python guy, so I decided to go that route. Simple, effective, and plenty of libraries to help me out.
Next up, read the bad words into a list. I used Python to get these words and put it in one array.
Next, I wrote a function to censor the text. This is where the magic happened. The function would take a string as input, split it into words, and then compare each word against my list of banned words. If a match was found, it’d replace the word with a bunch of asterisks, like “”.
- Lowercase everything: Made sure everything was lowercase before checking, so “SHIT” and “shit” would both get caught.
- Punctuation removal: Stripped out any punctuation marks, so things like “damn!” wouldn’t slip through.
- Partial word matching: This was tricky. I didn’t want to accidentally censor innocent words that just happened to contain a banned word. So, I made sure to only censor whole words.
After writing the censoring function, I created a testing program to make sure it worked. Testing is important, you know. I started with some simple sentences, then moved on to more complex ones with tricky punctuation and capitalization. Found a few bugs along the way, tweaked the code, and kept testing until I was satisfied.
The biggest challenge was handling variations of words. People get creative with their insults, so I had to account for things like misspellings, added letters, and other shenanigans. I didn’t want to go overboard and censor half the dictionary, but I also wanted to make sure the system was effective.
So, I implemented a fuzzy matching algorithm. This allowed the censor to catch words that were close to the banned words, even if they weren’t exact matches. I had to be careful with the sensitivity of the algorithm, though, to avoid false positives. I was using levenshtein distance algorithm.
Now that I have a censor program, I am integrating it with the app to have a better user experience. To avoid performance, I will try to save the processed data and use it directly.
Finally, I documented the whole process. Wrote up a README file explaining how the censor works, how to update the list of banned words, and how to adjust the sensitivity of the fuzzy matching algorithm. Always gotta leave a trail for the next guy (or myself, when I inevitably forget how it all works).
And that’s it! A working censor for my wrestling app. It ain’t perfect, but it’s a solid start. Now, back to building those body slams!
