Yet another general purpose Naive Bayesian classifier.
(under heavy development)

Naive Bayes Classifier is probably the most widely used text classifier, 
it's a supervised learning algorithm. It can be used to classify blog posts
or news articles into different categories like sports, entertainment and
so forth. 

Naive Bayes is a simple technique for constructing classifiers: models that
assign class labels to problem instances, represented as vectors of feature
values, where the class labels are drawn from some finite set. It is not a
single algorithm for training such classifiers, but a family of algorithms
based on a common principle: all naive Bayes classifiers assume that the value
of a particular feature is independent of the value of any other feature,
given the class variable. For example, a fruit may be considered to be an apple
if it is red, round, and about 10 cm in diameter. A naive Bayes classifier
considers each of these features to contribute independently to the probability
that this fruit is an apple, regardless of any possible correlations between
the color, roundness, and diameter features.

WWW: https://pypi.org/project/naiveBayesClassifier/