By Matthew A. Russell
How are you able to faucet into the wealth of social internet facts to find who’s making connections with whom, what they’re speaking approximately, and the place they’re positioned? With this extended and punctiliously revised variation, you’ll the best way to gather, learn, and summarize facts from all corners of the social net, together with fb, Twitter, LinkedIn, Google+, GitHub, e-mail, web pages, and blogs.
• hire the normal Language Toolkit, NetworkX, and different clinical computing instruments to mine renowned social sites
• practice complex text-mining suggestions, comparable to clustering and TF-IDF, to extract which means from human language information
• Bootstrap curiosity graphs from GitHub by way of getting to know affinities between humans, programming languages, and coding tasks
• reap the benefits of greater than two-dozen Twitter recipes, awarded in O’Reilly’s renowned "problem/solution/discussion" cookbook structure
the instance code for this special info technology booklet is maintained in a public GitHub repository. It’s designed to be simply available via a turnkey digital laptop that allows interactive studying with an easy-to-use selection of IPython Notebooks.
Read Online or Download Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More (2nd Edition) PDF
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Extra resources for Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More (2nd Edition)
3. Exploring Twitter’s API | 9 that may be attached to a tweet. Note that a place may be the actual location in which a tweet was authored, but it might also be a reference to the place described in a tweet. To make it all a bit more concrete, let’s consider a sample tweet with the following text: @ptwobrussell is writing @SocialWebMining, 2nd Ed. from his home office in Franklin, TN. me/16WJAf9. Although there is a place called Franklin, Tennessee that’s explicitly mentioned in the tweet, the places metadata associated with the tweet might include the location in which the tweet was authored, which may or may not be Franklin, Tennessee.
Word | Count | +--------------------------------+-------+ | #MentionSomeoneImportantForYou | 92 | | RT | 34 | | my | 10 | | , | 6 | | @justinbieber | 6 | | <3 | 6 | | My | 5 | | and | 4 | | I | 4 | | te | 3 | +--------------------------------+-------+ +----------------+-------+ | Screen Name | Count | +----------------+-------+ | justinbieber | 6 | | Kid_Charliej | 2 | | Cavillafuerte | 2 | | touchmestyles_ | 1 | | aliceorr96 | 1 | | gymleeam | 1 | | fienas | 1 | | nayely_1D | 1 | | angelchute | 1 | +----------------+-------+ +-------------------------------+-------+ | Hashtag | Count | +-------------------------------+-------+ | MentionSomeoneImportantForYou | 94 | | mentionsomeoneimportantforyou | 3 | | NoHomo | 1 | | Love | 1 | | MentionSomeOneImportantForYou | 1 | | MyHeart | 1 | | bebesito | 1 | +-------------------------------+-------+ A quick skim of the results reveals at least one marginally surprising thing: Justin Bieber is high on the list of entities for this small sample of data, and given his popularity with tweens on Twitter he may very well have been the “most important someone” for this trending topic, though the results here are inconclusive.
3. Exploring Twitter’s API | 21 The search_metadata field also contains a refresh_url value that can be used if you’d like to maintain and periodically update your collec‐ tion of results with new information that’s become available since the previous query. The next sample tweet shows the search results for a query for #MentionSomeoneIm‐ portantForYou. Take a moment to peruse (all of) it. As I mentioned earlier, there’s a lot more to a tweet than meets the eye. The particular tweet that follows is fairly represen‐ tative and contains in excess of 5 KB of total content when represented in uncompressed JSON.