http://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter

Using the concepts in this chapter, you can create a spam detection method that is able to view a social media post and determine whether it is spam or not. Try this out by first creating a dataset of spam/not-spam posts, implementing the text mining algorithms, and then evaluating them.

One important consideration with spam detection is the false-positive/false-negative ratio. Many people would prefer to have a couple of spam messages slip through, rather than miss out on a legitimate message because the filter was too aggressive in stopping the spam. In order to turn your method for this, you can use a Grid Search with the f1-score as the evaluation criteria. See the above link for information on how to do this.

http://www.nltk.org/book/ch05.html

The techniques we used in this chapter were quite lightweight compared to some of the linguistic models employed in other areas. For example, part-of-speech tagging can help disambiguate word forms, allowing for higher accuracy. The book that comes with NLTK has a chapter on this, linked above. The whole book is well worth reading too.