lwebzem249
It is interesting to see how machine learning is applied for different tasks. So here will be links pointing to articles, posts with examples of building and using.
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lwebzem249
Here is the first example:

Profanity-check - A fast, robust Python library to check for profanity or offensive language in strings. 

Profanity-check uses a linear SVM model trained on 200k human-labeled samples of clean and profane text strings. Its model is simple but surprisingly effective, meaning profanity-check is both robust and extremely performant.   The author analyzed existing profanity checkers and found that they can be improved significantly. And so here is the new profanity-check library distributed under MIT. Below are the links:

https://medium.com/@victorczhou/building-a-better-profanity-detection-library-with-scikit-learn-3638b2f2c4c2

link to code                https://github.com/vzhou842/profanity-check
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lwebzem249
Another example: Keyword extraction is often used in information processing. For example we might want tag documents for easy retrieving later.  Here is the example how it can be done with TF-IDF and Python’s Scikit-Learn library.

http://kavita-ganesan.com/extracting-keywords-from-text-tfidf/#.XWL6EehKjIV

Tutorial: Extracting Keywords with TF-IDF and Python’s Scikit-Learn   By Kavita Ganesan

This is very interesting and detailed keyword extraction tutorial with practical example using a stack overflow dataset.  The tutorial also has some tips how to apply this machine learning algorithm on real tasks.
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