By Matthew A. Russell

How are you able to faucet into the wealth of social internet info to find who’s making connections with whom, what they’re speaking approximately, and the place they’re positioned? With this improved and punctiliously revised version, you’ll tips on how to collect, learn, and summarize facts from all corners of the social internet, together with fb, Twitter, LinkedIn, Google+, GitHub, e-mail, web pages, and blogs.

• hire the typical Language Toolkit, NetworkX, and different clinical computing instruments to mine well known social websites
• observe complex text-mining options, resembling clustering and TF-IDF, to extract which means from human language information
• Bootstrap curiosity graphs from GitHub through studying affinities between humans, programming languages, and coding tasks
• construct interactive visualizations with D3.js, an awfully versatile HTML5 and JavaScript toolkit
• make the most of greater than two-dozen Twitter recipes, provided in O’Reilly’s renowned "problem/solution/discussion" cookbook layout

the instance code for this distinctive info technological know-how e-book is maintained in a public GitHub repository. It’s designed to be simply available via a turnkey digital computer that allows interactive studying with an easy-to-use selection of IPython Notebooks.

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Additional info for Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More (2nd Edition)

Sample text

MentionSomeoneImportantForYou @Louis_Tomlinson", "#MentionSomeoneImportantForYou @Delta_Universe" ] [ "KathleenMariee_", "AhhlicksCruise", "itsravennn_cx", "kandykisses_13", "BMOLOGY" ] [ "MentionSomeOneImportantForYou", "MentionSomeoneImportantForYou", "MentionSomeoneImportantForYou", "MentionSomeoneImportantForYou", "MentionSomeoneImportantForYou" ] [ "\u201c@KathleenMariee_:", "#MentionSomeOneImportantForYou", "@AhhlicksCruise", ",", "@itsravennn_cx" ] As expected, #MentionSomeoneImportantForYou dominates the hashtag output.

Split() ] # Explore the first 5 items for each... dumps(words[0:5], indent=1) Sample output follows; it displays five status texts, screen names, and hashtags to provide a feel for what’s in the data. 28 | Chapter 1: Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More In Python, syntax in which square brackets appear after a list or string value, such as status_texts[0:5], is indicative of slicing, whereby you can easily extract items from lists or substrings from strings.

The output also provides a few commonly occurring screen names that are worth investigating. 2. Analyzing Tweets and Tweet Entities with Frequency Analysis Virtually all analysis boils down to the simple exercise of counting things on some level, and much of what we’ll be doing in this book is manipulating data so that it can be counted and further manipulated in meaningful ways. 4. Analyzing the 140 Characters | 29 nipulation that strives to find what may be a faint signal in noisy data. Whereas we just extracted the first 5 items of each unranked list to get a feel for the data, let’s now take a closer look at what’s in the data by computing a frequency distribution and looking at the top 10 items in each list.

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