Social Media Mining with Python
Packt Publishing Limited (Verlag)
978-1-78528-115-0 (ISBN)
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What You Will Learn * Learn the techniques to effectively analyze large volumes of social media data * Make RESTful calls to API services of different social media websites and authenticate clients using OAuth * Work with the Natural Language Toolkit, Sklearn, NetworkX, Pandas, and other popular Python libraries * Perform sentiment analysis of social media text * Leverage Spark to collect, process, and analyze social media data * Store and read data from cloud storage such as DynamoDB and S3 * Utilize Python visualization libraries to gain more insights and visualize data In Detail Millions of Internet users take to social networks to discuss and review products, provide opinions, and express their viewpoints on various topics. Social media mining helps with the extraction and discovery of information from huge amounts of data produced on social media websites. It also aids in the process of making data-driven decisions by drawing actionable insights from social data. This process can be automated using Python to save time and costs. The book will get you started and get you ready to explore and mine the wide horizon of social media data along with its nuances.
It starts by giving you an overview of social media mining and the challenges involved. You will learn about standard authentication techniques to query social media websites. You will also see how to work with the LinkedIn and Facebook APIs and collect and analyze data using various tools. Moving on, you will find out how to collect data related to a social media campaign from YouTube and analyze it. Then, you will discover how to perform sentiment analysis on Twitter and crawl blogs. Finally, you will be introduced to various cloud services and storage options for big data. By the end of this book, you will be able to use Python to extract meaningful information and insights from large datasets found on social media websites such as Twitter, Facebook, and blogs.
Debanjan Mahata is a Senior Research Associate in the Big Data and Analytics division of Infosys Technologies Ltd. He holds a Doctorate degree in Information Quality from University of Arkansas at Little Rock. He is extremely passionate about social media mining. His current work revolves around developing machine learning models and data pipelines that enable the extraction and exploration of valuable information hidden in large volumes of unstructured social media data. He has a deep research interest in understanding the quality of information available in social media channels during real-life events and how to retrieve the most useful nuggets of information from the huge stream of unstructured social data. Saket Saurabh is a software engineer at Amazon.com. He works over different aspects of social media integration within Amazon's shopping experience. He graduated from the Indian Institute of technology (BHU) with a B.Tech in Computer Science and Engineering. He also contributes to various open source projects in his free time. In the past, he has contributed to Elgg, an open source social network engine.
| Erscheinungsdatum | 11.07.2017 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 190 x 235 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Mathematik / Informatik ► Informatik ► Web / Internet | |
| ISBN-10 | 1-78528-115-1 / 1785281151 |
| ISBN-13 | 978-1-78528-115-0 / 9781785281150 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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