Python Data Analysis Cookbook

Chapter 8. Text Mining and Social Network Analysis

In this chapter, we will cover the following recipes:

• Creating a categorized corpus
• Tokenizing news articles in sentences and words
• Stemming, lemmatizing, filtering, and TF-IDF scores
• Recognizing named entities
• Extracting topics with non-negative matrix factorization
• Implementing a basic terms database
• Computing social network density
• Calculating social network closeness centrality
• Determining the betweenness centrality
• Estimating the average clustering coefficient
• Calculating the assortativity coefficient of a graph
• Getting the clique number of a graph
• Creating a document graph with cosine similarity

Introduction

Humans have communicated through language for thousands of years. Handwritten texts have been around ...