Mastering Social Media Mining with Python

Cover
Copyright
Credits
About the Author
About the Reviewer
www.PacktPub.com
Table of Contents
Preface
Chapter 1: Social Media, Social Data, and Python
	Getting started
	Social media – challenges and opportunities
		Opportunities
		Challenges
		Social media mining techniques
	Python tools for data science
		Python development environment setup
			pip and virtualenv
			Conda, Anaconda, and Miniconda
		Efficient data analysis
		Machine learning
		Natural language processing
		Social network analysis
		Data visualization
	Processing data in Python
	Building complex data pipelines
	Summary
Chapter 2: #MiningTwitter – Hashtags, Topics, and Time Series
	Getting started
	The Twitter API
		Rate limits
		Search versus Stream
	Collecting data from Twitter
		Getting tweets from the timeline
		The structure of a tweet
		Using the Streaming API
	Analyzing tweets – entity analysis
	Analyzing tweets – text analysis
	Analyzing tweets – time series analysis
	Summary
Chapter 3: Users, Followers, and Communities on Twitter
	Users, friends, and followers
		Back to the Twitter API
		The structure of a user profile
		Downloading your friends\' and followers\' profiles
		Analysing your network
		Measuring influence and engagement
	Mining your followers
	Mining the conversation
	Plotting tweets on a map
		From tweets to GeoJSON
		Easy maps with Folium
	Summary
Chapter 4: Posts, Pages, and User Interactions on Facebook
	The Facebook Graph API
		Registering your app
		Authentication and security
		Accessing the Facebook Graph API with Python
	Mining your posts
		The structure of a post
		Time frequency analysis
	Mining Facebook Pages
		Getting posts from a Page
			Facebook Reactions and the Graph API 2.6
		Measuring engagement
		Visualizing posts as a word cloud
	Summary
Chapter 5: Topic Analysis on Google+
	Getting started with the Google+ API
		Searching on Google+
	Embedding the search results in a web GUI
		Decorators in Python
		Flask routes and templates
	Notes and activities from a Google+ page
	Text analysis and TF-IDF on notes
		Capturing phrases with n-grams
	Summary
Chapter 6: Questions and Answers on Stack Exchange
	Questions and answers
	Getting started with the Stack Exchange API
		Searching for tagged questions
		Searching for a user
	Working with Stack Exchange data dumps
	Text classification for question tags
		Supervised learning and text classification
		Classification algorithms
			Naive Bayes
			k-Nearest Neighbor
			Support Vector Machines
		Evaluation
		Performing text classification on Stack Exchange data
		Embedding the classifier in a real-time application
	Summary
Chapter 7: Blogs, RSS, Wikipedia, and Natural Language Processing
	Blogs and NLP
	Getting data from blogs and websites
		Using the WordPress.com API
		Using the Blogger API
		Parsing RSS and Atom feeds
		Getting data from Wikipedia
		A few words about web scraping
	NLP Basics
		Text preprocessing
			Sentence boundary detection
			Word tokenization
			Part-of-speech tagging
			Word normalization
				Case normalization
				Stemming
				Lemmatization
				Stop word removal
				Synonym mapping
		Information extraction
	Summary
Chapter 8: Mining All the Data!
	Many social APIs
	Mining videos on YouTube
	Mining open source software on GitHub
	Mining local businesses on Yelp
	Building a custom Python client
		HTTP made simple
	Summary
Chapter 9: Linked Data and the Semantic Web
	A Web of Data
		Semantic Web vocabulary
		Microformats
		Linked Data and Open Data
		Resource Description Framework
		JSON-LD
		Schema.org
	Mining relations from DBpedia
	Mining geo coordinates
		Extracting geodata from Wikipedia
		Plotting geodata on Google Maps
	Summary
Index