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 ...
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