Social Media Data Mining and Analytics

Harness the power of social media to predict customer behavior and improve sales

Social media is the biggest source of Big Data. Because of this, 90% of Fortune 500 companies are investing in Big Data initiatives that will help them predict consumer behavior to produce better sales results. Social Media Data Mining and Analytics shows analysts how to use sophisticated techniques to mine social media data, obtaining the information they need to generate amazing results for their businesses.

Social Media Data Mining and Analytics isn't just another book on the business case for social media. Rather, this book provides hands-on examples for applying state-of-the-art tools and technologies to mine social media - examples include Twitter, Wikipedia, Stack Exchange, LiveJournal, movie reviews, and other rich data sources. In it, you will learn:

• The four key characteristics of online services-users, social networks, actions, and content
• The full data discovery lifecycle-data extraction, storage, analysis, and visualization
• How to work with code and extract data to create solutions
• How to use Big Data to make accurate customer predictions
• How to personalize the social media experience using machine learning

Using the techniques the authors detail will provide organizations the competitive advantage they need to harness the rich data available from social media platforms.

Author: Gabor Szabo, Gungor Polatkan, P. Oscar Boykin
Publisher: Wiley
Published: 10/23/2018
Pages: 352
Binding Type: Paperback
Weight: 1.00lbs
Size: 9.20h x 7.50w x 0.70d
ISBN: 9781118824856

About the Author

GABOR SZABO, PHD, is a Senior Staff Software Engineer at Tesla and a former data scientist at Twitter, where he focused on predicting user behavior and content popularity in crowdsourced online services, and on modeling large-scale content dynamics. He also authored the PyCascading data processing library.

GUNGOR POLATKAN, PHD, is a Tech Lead/Engineering Manager designing and implementing end-to-end machine learning and artificial intelligence offline/online pipelines for the LinkedIn Learning relevance backend. He was previously a machine learning scientist at Twitter, where he worked on topics such as ad targeting and user modeling.

P. OSCAR BOYKIN, PHD, is a software engineer at Stripe where he works on machine learning infrastructure. He was previously a Senior Staff Engineer at Twitter, where he worked on data infrastructure problems. He is coauthor of the Scala big-data libraries Algebird, Scalding and Summingbird.

ANTONIOS CHALKIOPOULOS, MSC, is a Distributed Systems Specialist. A system engineer who has delivered fast/big data projects in media, betting, and finance, he is now leading the effort on the Lenses platform for data streaming as a co-founder and CEO at https: //lenses.stream.

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