Learning from text – Naive Bayes for Natural Language Processing

In this recipe, we show how to handle text data with scikit-learn. Working with text requires careful preprocessing and feature extraction. It is also quite common to deal with highly sparse matrices.

We will learn to recognize whether a comment posted during a public discussion is considered insulting to one of the participants. We will use a labeled dataset from Impermium, released during a Kaggle competition.

Getting ready

Download the Troll dataset from the book's GitHub repository at https://github.com/ipython-books/cookbook-data.

This dataset was obtained from Kaggle, at www.kaggle.com/c/detecting-insults-in-social-commentary.

How to do it...

1. Let's import our libraries:

   In [1]: import numpy as np
           import pandas as pd
           import sklearn
           import sklearn.cross_validation as cv
           import sklearn.grid_search as gs
           import sklearn.feature_extraction.text as text
           import sklearn.naive_bayes as nb...