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Mastering Social Media Mining with Python


Mastering Social Media Mining with Python Marco Bonzanini

Acquire and analyze data from all corners of the social web with Python. This book is for intermediate Python developers who want to engage with the use of public APIs to collect data from social media platforms and perform statistical analysis in order to produce useful insights from the data. The book assumes a basic understanding of Python standard library and provides practical examples to guide you towards the creation of your data analysis project based on social data.

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, 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. This book is the outcome of a long journey that goes beyond the mere content preparation. Many people have contributed in different ways to shape the final result. Firstly, I would like to thank the team at Packt Publishing, particularly Sonali Vernekar and Siddhesh Salvi, for giving me the opportunity to work on this book and for being so helpful throughout the whole process. I would also like to thank Dr. Weiai “Wayne” Xu for reviewing the content of this book and suggesting many improvements. Many colleagues and friends, through casual conversations, deep discussions, and previous projects, strengthened the quality of the material presented in this book. Special mentions go to Dr. Miguel Martinez-Alvarez, Marco Campana, and Stefano Campana. I'm also happy to be part of the PyData London community, a group of smart people who regularly meet to talk about Python and data science, offering a stimulating environment. Last but not least, a distinct special mention goes to Daniela, who has encouraged me during the whole journey, sharing her thoughts, suggesting improvements, and providing a relaxing environment to go back to after work.

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