September 2014
Intermediate to advanced
512 pages
12h 39m
English
Content preview from IPython Interactive Computing and Visualization CookbookBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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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...
- Let's import our libraries:
In [1]: import ...
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