Natural Language Processing and Sentiment Analysis with Python

0. Introduction to NLP and Sentiment Analysis

1. Natural Language Processing with NTLK

2. Intro to NTLK, Part 2

3. Build a sentiment analysis program

4. Sentiment Analysis with Twitter

5. Analysing the Enron Email Corpus

6. Build a Spam Filter using the Enron Corpus

In spite of the big, complicated name, Natural Language Processing is actually not that hard to understand. NLP is used to make computers understand human language, and usually uses techniques taken from machine learning.

Originally created for AI research (computers like Dave from 2001: A Space Odysseys that could talk to humans), it is now used for less glamorous but more practically useful fields, like sentiment analysis, summarising articles etc.

Python has had great support for NLP for a long time, including a completely free book.

Not only that, there are many API’s that allow you to NLP and machine learning features without writing any code. Here’s a list of some of them. I still recommend going through this tutorial, even if you are going to use the APIs, so that you know what you are doing.


Only a basic knowledge of Python is needed.

If you know nothing about machine learning, please look at my tutorial on Machine Learning before you proceed.


As usual, you’ll have both HD-videos and text. The last section, Part 4, is an exercise for you, to check if you have learnt anything, and whether you can apply the knowledge.

FUN: Do you think the movie below got positive or negative reviews? We will be using Python to find out!

Ready? Lets get started