Great news, my book on data mining for social media is finally out!
The title is Mastering Social Media Mining with Python. I’ve been working with Packt Publishing over the past few months, and in July the book has been finalised and released.
- ebook and paperback on Packt Publishing (the publisher)
- ebook and paperback on Amazon.com and Amazon UK
- Companion code for the book on my GitHub
As part of Packt’s Mastering series, the book assumes the readers already have some basic understanding of Python (e.g. for loops and classes), but more advanced concepts are discussed with examples. No particular experience with Social Media APIs and Data Mining is required. With 300+ pages, by the end of the book, the readers should be able to build their own data mining projects using data from social media and Python tools.
A bird’s eye view on the content:
- Social Media, Social Data and Python
- Introduction on Social Media and Social Data: challenges and opportunities
- Introduction on Python tools for Data Science
- Overview on the use of public APIs to interact with social media platforms
- #MiningTwitter: Hashtags, Topics and Time Series
- Interacting with the Twitter API in Python
- Twitter data: the anatomy of a tweet
- Entity analysis, text analysis, time series analysis on tweets
- Users, Followers, and Communities on Twitter
- Analysing who follows whom
- Mining your followers
- Mining communities
- Visualising tweets on a map
- Posts, Pages and User Interactions on Facebook
- Interacting the Facebook Graph API in Python
- Mining you posts
- Mining Facebook Pages
- Topic analysis on Google Plus
- Interacting with the Google Plus API in Python
- Finding people and pages on G+
- Analysis of notes and activities on G+
- Questions and Answers on Stack Exchange
- Interacting with the StackOverflow API in Python
- Text classification for question tags
- Blogs, RSS, Wikipedia, and Natural Language Processing
- Blogs and web pages as social data Web scraping with Python
- Basics of text analytics on blog posts
- Information extraction from text
- Mining All the Data!
- Interacting with many other APIs and types of objects
- Examples of interaction with YouTube, Yelp and GitHub
- Linked Data and the Semantic Web
- The Web as Social Media
- Mining relations from DBpedia
- Mining geo coordinates
The detailed table of contents is shown on the Packt Pub’s page. Chapter 2 is also offered as free sample.
Please have a look at the companion code for the book on my GitHub, so you can have an idea of the applications discussed in the book.