Data In Action Series 4: Text Classification

The dataset is made up of newsgroup text.

There is the word cloud of the first document which belongs to the type of soc.religion.christian.

• There are 3 methods (such as count, tf, tf-idf) to extract features from text.

• Different features extraction method have different effect to text classification.

• For our example data set:

• the combination of count method and multinomial naive bayes model are best;

• the combination of tf-idf method and support vector machine which kernel is linear are better than other models.

Please refer the whole python code in here.

Welcome your advice and suggestion!

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