Bibliograph

Mining the social web, Matthew A. Russell

Type

Label

Mining the social web, Matthew A. Russell

Language

eng

Bibliography note

Includes bibliographical references and index

Illustrations

illustrations

Index

index present

Literary Form

non fiction

Main title

Mining the social web

Nature of contents

bibliography

Oclc number

856195868

Responsibility statement

Matthew A. Russell

Summary

"How can you tap into the wealth of social web data to discover who's making connections with whom, what they're talking about, and where they're located? With this expanded and thoroughly revised edition, you'll learn how to acquire, analyze, and summarize data from all corners of the social web, including Facebook, Twitter, LinkedIn, Google+, GitHub, email, websites, and blogs. Employ the Natural Language Toolkit, NetworkX, and other scientific computing tools to mine popular social web sites. Apply advanced text-mining techniques, such as clustering and TF-IDF, to extract meaning from human language data. Bootstrap interest graphs from GitHub by discovering affinities among people, programming languages, and coding projects. Build interactive visualizations with D3.js, an extraordinarily flexible HTML5 and JavaScript toolkit. Take advantage of more than two-dozen Twitter recipes, presented in O'Reilly's popular "problem/solution/discussion" cookbook format The example code for this unique data science book is maintained in a public GitHub repository. It's designed to be easily accessible through a turnkey virtual machine that facilitates interactive learning with an easy-to-use collection of IPython Notebooks."--, Provided by publisher

Table Of Contents

1. Mining twitter: exploring trending topics, discovering what people are talking about, and more -- 2. Mining facebook: analyzing fan pages, examining friendships, and more -- 3. Mining linkdln: faceting job titles, clustering colleagues, and more -- 4. Mining google+: computing documents similarity, extracting collocations, and more -- 5. Mining web pages: Using natural language processing to understand human language, summarize blog posts, and more -- 6. Mining mailboxes: analyzing who's talking to whom about what, how often, and more -- 7. Mining github: inspecting software collaboration habits, building interest graphs, and more -- 8. Mining the semantically marked-up web: extracting microformats, inferencing over RDF, and more -- 9. Twitter cookbook

Creator