Mastering Social Media Mining with Python

Description

Key Features Make sense of highly unstructured social media data with the help of the insightful use cases provided in this guide Use this easy-to-follow, step-by-step guide to apply analytics to complicated and messy social data This is your one-stop solution to fetching, storing, analyzing, and visualizing social media data Book Description Your social media is filled with a wealth of hidden data – unlock it with the power of Python. Transform your understanding of your clients and customers when you use Python to solve the problems of understanding consumer behavior and turning raw data into actionable customer insights. This book will help you acquire and analyze data from leading social media sites. It will show you how to employ scientific Python tools to mine popular social websites such as Facebook, Twitter, Quora, and more. Explore the Python libraries used for social media mining, and get the tips, tricks, and insider insight you need to make the most of them. Discover how to develop data mining tools that use a social media API, and how to create your own data analysis projects using Python for clear insight from your social data. What you will learn Interact with a social media platform via their public API with Python Store social data in a convenient format for data analysis Slice and dice social data using Python tools for data science Apply text analytics techniques to understand what people are talking about on social media Apply advanced statistical and analytical techniques to produce useful insights from data Build beautiful visualizations with web technologies to explore data and present data products About the Author Marco Bonzanini is a data scientist based in London, United Kingdom. He holds a PhD in information retrieval from Queen Mary University of London. He specializes in text analytics and search applications, and over the years, he has enjoyed working on a variety of information management and data science problems. He maintains a personal blog at http://marcobonzanini.com, where he discusses different technical topics, mainly around Python, text analytics, and data science. When not working on Python projects, he likes to engage with the community at PyData conferences and meet-ups, and he also enjoys brewing homemade beer. Table of Contents Social Media, Social Data, and Python MiningTwitter – Hashtags, Topics, and Time Series Users, Followers, and Communities on Twitter Posts, Pages, and User Interactions on Facebook Topic Analysis on Google+ Questions and Answers on Stack Exchange Blogs, RSS, Wikipedia, and Natural Language Processing Mining All the Data! Linked Data and the Semantic Web 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