Book Image

Mastering Social Media Mining with Python

By : Marco Bonzanini
Book Image

Mastering Social Media Mining with Python

By: Marco Bonzanini

Overview of this book

Your social media is filled with a wealth of hidden data – unlock it with the power of Python. Transform your understanding of your clients and customers when you use Python to solve the problems of understanding consumer behavior and turning raw data into actionable customer insights. This book will help you acquire and analyze data from leading social media sites. It will show you how to employ scientific Python tools to mine popular social websites such as Facebook, Twitter, Quora, and more. Explore the Python libraries used for social media mining, and get the tips, tricks, and insider insight you need to make the most of them. Discover how to develop data mining tools that use a social media API, and how to create your own data analysis projects using Python for clear insight from your social data.
Table of Contents (10 chapters)

Mining relations from DBpedia


DBpedia is one of the best-known sources of Linked Data. Based on Wikipedia, it augments the content of the popular wiki-based encyclopedia with semantic connections between entities. The structured information from DBpedia can be accessed via the Web using a SQL-like language called SPARQL, a semantic query language for RDF.

In Python, while we have the option of querying the database with SPARQL, we can take advantage of the RDFLib package, a library used to work with RDF.

From our virtual environment, we can install it using pip:

$ pip install rdflib

Given the complexity of the Semantic Web topic, we prefer to dig into an example so that you can have the flavor of the capabilities of DBpedia, and at the same time, get an overview of how to use the RDFLib package.

The rdf_summarize_entity.py script looks up for a given entity and tries to output its summary to the user:

# Chap09/rdf_summarize_entity.py 
from argparse import ArgumentParser 
import rdflib...