Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More

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(2013). Sebastopol, CA: O’Reilly Media, Inc., 448 pp

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Mining data on Facebook with Python: 1- Setting up our app for mining data on Facebook

It covers matplotlib , prettytable and other Python packages. I have used Twitter APIs extensively and found this chapter very useful and well written. It demonstrates how to use facebook-sdk package to make FQL queries.

Mining the Social Web | Transforming Curiosity into Insight

Other examples include computing overlapping likes in social network, analyzing mutual friendships and visualizing with D3. It also covers data normalization and similarity computation techniques, visualizing locations with cartograms, clustering algorithms. I think this was one of the most detailed and informative chapters of the book and I thoroughly enjoyed reading it. It covers TF-IDF which stands for term frequency-inverse document frequency, it also covers algorithms to find similar documents, visualizing document similarities with martix diagrams, contingency tables and scoring functions.

Most importantly this chapter covered so many techniques to extract information from unstructured text with Information retrieval techniques in meticulous detail.

PDF Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More

Chapter 5 discusses context driven techniques and goes in the semantics of human language data. It covers the Boilerpipe library to extract text from a web page, feedparser to extract from RSS feeds, web crawling techniques, EOS detection,Tokenization, Part-of-speech tagging and Chunking. Chapter 6 covers basic semantics of mbox, mail headers, how to convert mbox to json and import to MongoDB, querying by date time, analyzing patterns in sending and receiving messages using Enron data.

It also covers how to analyse your own mail data and access Gmail data with OAuth. This chapter is built on previous ones and covers a lot from basic mbox to mining mboxes to slicing and dicing the data. It covers an example with PyGithub to access the query objects. Additionally it covers the concept of an interest graph and how to construct an interest graph from GitHub data and how to model property graphs with NetworkX. I found the section on getting the repositories from a graph, finding the programming languages for each user and the GitHubArchive very educative.

This chapter talks about microformats an examples covered using Google's Data testing tool to access semantic markup from web pages for eg.

LinkedIn's profiles, Wikipedia , About. This is a whole section dedicated to recipes for mining Twitter's accessible APIs due to the openness and emerging popularity. I have done a few sentiment analysis related projects with Twitter API and found this really helpful for people who want to dig deeper in the API. This covers different recipes in form of problems, solutions and discussions from topics ranging from OAuth, finding the populatr trends, searching tweets.

I thoroughly enjoyed reading and reviewing "Mining the Social Web". It has detailed information for everyone from an Information Retrieval IR enthusiast, a data scientist, an analyst or just a curious reader looking to learn the different API available out there to extract, mine, visualize and interpret the data and explore the infinite possiblities by using the diverse building blocks together.

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