This recipe shows how to graph data in Python using the Matlablib library.

This recipe shows how to create a basic concordance tool in Python.

This recipe shows how to perform Collocation on a text, finding which words collocate with a search term.

This recipe shows how to scrape comments from a YouTube video to analyze.

This recipe shows how to conduct dictionary-based sentiment analysis on a collection of passages, such as tweets or reviews. It uses pre-existing dictionaries of positive and negative words, and loads a text file of passages to analyze.

This recipe shows how to analyze the sentiment of a simple passage of text, such as a tweet.

This is a recipe for analyzing the mood or opinion of a text or corpus.

Let's say that you have a large collection of texts and you want to use a computer to help you classify those texts into two or more groups, such as "Philosophical" and "Other". One technique to accomplish this task is to use supervised learning whereby you train a computer to classify texts for you. Training involves manually classifying a subset of your texts, having the computer analyze features in each subset, and then having the computer try to classify texts that haven't already been classified.

This is a recipe for looking at the changes that have taken place in a wikipedia article over time, and generating a corpus of the different edited versions.

The technique known as "indexing" plays a fundamental role in search engines like Google and Yahoo, and can help researchers rapidly expedite their data analysis. This recipe will describe the steps one can follow in order to index data with the Python package Whoosh.