The program has analysed 300 east and 300 west coast lyrics and looked at the words used in those to train a Naive Bayesian Classifier using the Natural Language Processing Toolkit for Python. It then lets you put in a twitter name and loads its latest 174 tweets and classifies that bag of words to the classifier to let you know if you tweet like west coast or east coast rappers rapped in the nineties.
I finally got NLTK to work online after trying different hosting companies. Decided to learn Bottle to build the web version of the program and it was a breeze, which was awesome as I am really a beginner in programming.
Loved to getting my first real project done and working without quitting before it was finished. Extended my knowledge in jQuery and css a bit and overall had a lot of fun.
I wanted to ask you guys what you think of it and if you have any suggestions both content and design wise.
I found that after getting it to work I was kinda lost in the design. It's funny: when the code works it works, but designing seems to be a never ending job of improving and not being satisfied.
aside from that, great work!
Furthermore, the words listed are certainly not necessarily words you used, but just the 50 most differentiating words between east and west coast. 'compton' is used way more in west coast lyrics than east for example. It's just there to see some fun differences not related to your personal twitter stream. At last, in total the classifier looks for not 50, but 1000 words in classifying your tweets. Hope this explained it a bit.
Thanks for pointing out the flaw, I'll try to implement some more descriptive error messages for different errors.
