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Mastering Social Media Mining With Python

Key FeaturesMake sense of highly unstructured social media data with the help of the insightful use cases provided in this guideUse this easy-to-follow, step-by-step guide to apply analytics to complicated and messy social dataThis is your one-stop solution to fetching, storing, analyzing, and visualizing social media dataBook DescriptionYour 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 learnInteract with a social media platform via their public API with PythonStore social data in a convenient format for data analysisSlice and dice social data using Python tools for data scienceApply text analytics techniques to understand what people are talking about on social mediaApply advanced statistical and analytical techniques to produce useful insights from dataBuild beautiful visualizations with web technologies to explore data and present data productsAbout the AuthorMarco 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, 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 ContentsSocial Media, Social Data, and Python#MiningTwitter – Hashtags, Topics, and Time SeriesUsers, Followers, and Communities on TwitterPosts, Pages, and User Interactions on FacebookTopic Analysis on Google+Questions and Answers on Stack ExchangeBlogs, RSS, Wikipedia, and Natural Language ProcessingMining All the Data!Linked Data and the Semantic Web

File Size: 14737 KB

Print Length: 338 pages

Publisher: Packt Publishing; 1 edition (July 29, 2016)

Publication Date: July 29, 2016

Sold by:  Digital Services LLC

Language: English


Text-to-Speech: Enabled

X-Ray: Not Enabled

Word Wise: Not Enabled

Lending: Not Enabled

Enhanced Typesetting: Enabled

Best Sellers Rank: #428,939 Paid in Kindle Store (See Top 100 Paid in Kindle Store) #179 in Kindle Store > Kindle eBooks > Computers & Technology > Programming > Python #305 in Books > Computers & Technology > Databases & Big Data > Data Modeling & Design #359 in Books > Computers & Technology > Databases & Big Data > Data Mining

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