Social Media Mining with Python

Leverage the power of social media to gain insight into global trends using PythonAbout This BookLearn to use APIs for popular social media websites and collect data from Twitter, Facebook, Pinterest, Instagram, Foursquare, LinkedIn, and RedditPerform sentiment analysis using the readily available machine learning and basic deep learning Python libraries to extract valuable information from short social media textThis hands-on guide is packed with practical, real-life examples to help you implement social media miningWho This Book Is ForIf you are a Python programmer who wants to understand the details of social media websites' APIs, or a social science major or data scientist who wants work with social media, then this book is for you. A basic Python programming knowledge is sufficient for you to get going.What You Will LearnLearn the techniques to effectively analyze large volumes of social media dataMake RESTful calls to API services of different social media websites and authenticate clients using OAuthWork with the Natural Language Toolkit, Sklearn, NetworkX, Pandas, and other popular Python librariesPerform sentiment analysis of social media textLeverage Spark to collect, process, and analyze social media dataStore and read data from cloud storage such as DynamoDB and S3Utilize Python visualization libraries to gain more insights and visualize dataIn DetailMillions 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.

Author: Debanjan Mahata

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