Python social media analytics : analyze and visualize data from Twitter, YouTube, GitHub, and more

Python social media analytics analyze and visualize data from Twitter, YouTube, GitHub, and more

Leverage the power of Python to collect, process, and mine deep insights from social media data About This Book Acquire data from various social media platforms such as Facebook, Twitter, YouTube, GitHub, and more Analyze and extract actionable insights from your social data using various Python too...

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Detalles Bibliográficos
Otros Autores: Chatterjee, Siddhartha (Professor of political science), author (author), Krystyanczuk, Michal, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England : Packt 2017.
Edición:1st edition
Materias:
Python (Computer program language)
Data mining.
Social media.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630730306719
Tabla de Contenidos:
  • Cover
  • Copyright
  • Credits
  • About the Authors
  • Acknowledgments
  • About the Reviewer
  • www.PacktPub.com
  • Customer Feedback
  • Table of Contents
  • Preface
  • Chapter 1: Introduction to the Latest Social Media Landscape and Importance
  • Introducing social graph
  • Notion of influence
  • Social impacts
  • Platforms on platform
  • Delving into social data
  • Understanding semantics
  • Defining the semantic web
  • Exploring social data applications
  • Understanding the process
  • Working environment
  • Defining Python
  • Selecting an IDE
  • Illustrating Git
  • Getting the data
  • Defining API
  • Scraping and crawling
  • Analyzing the data
  • Brief introduction to machine learning
  • Techniques for social media analysis
  • Setting up data structure libraries
  • Visualizing the data
  • Getting started with the toolset
  • Summary
  • Chapter 2: Harnessing Social Data - Connecting, Capturing, and Cleaning
  • APIs in a nutshell
  • Different types of API
  • RESTful API
  • Stream API
  • Advantages of social media APIs
  • Limitations of social media APIs
  • Connecting principles of APIs
  • Introduction to authentication techniques
  • What is OAuth?
  • User authentication
  • Application authentication
  • Why do we need to use OAuth?
  • Connecting to social network platforms without OAuth
  • OAuth1 and OAuth2
  • Practical usage of OAuth
  • Parsing API outputs
  • Twitter
  • Creating application
  • Selecting the endpoint
  • Using requests to connect
  • Facebook
  • Creating an app and getting an access token
  • Selecting the endpoint
  • Connect to the API
  • GitHub
  • Obtaining OAuth tokens programmatically
  • Selecting the endpoint
  • Connecting to the API
  • YouTube
  • Creating an application and obtaining an access token programmatically
  • Selecting the endpoint
  • Connecting to the API
  • Pinterest
  • Creating an application.
  • Selecting the endpoint
  • Connecting to the API
  • Basic cleaning techniques
  • Data type and encoding
  • Structure of data
  • Pre-processing and text normalization
  • Duplicate removal
  • MongoDB to store and access social data
  • Installing MongoDB
  • Setting up the environment
  • Starting MongoDB
  • MongoDB using Python
  • Summary
  • Chapter 3: Uncovering Brand Activity, Popularity, and Emotions on Facebook
  • Facebook brand page
  • The Facebook API
  • Project planning
  • Scope and process
  • Data type
  • Analysis
  • Step 1 - data extraction
  • Step 2 - data pull
  • Step 3 - feature extraction
  • Step 4 - content analysis
  • Keywords
  • Extracting verbatims for keywords
  • User keywords
  • Brand posts
  • User hashtags
  • Noun phrases
  • Brand posts
  • User comments
  • Detecting trends in time series
  • Maximum shares
  • Brand posts
  • User comments
  • Maximum likes
  • Brand posts
  • Comments
  • Uncovering emotions
  • How to extract emotions?
  • Introducing the Alchemy API
  • Connecting to the Alchemy API
  • Setting up an application
  • Applying Alchemy API
  • How can brands benefit from it?
  • Summary
  • Chapter 4: Analyzing Twitter Using Sentiment Analysis and Entity Recognition
  • Scope and process
  • Getting the data
  • Getting Twitter API keys
  • Data extraction
  • REST API Search endpoint
  • Rate Limits
  • Streaming API
  • Data pull
  • Data cleaning
  • Sentiment analysis
  • Customized sentiment analysis
  • Labeling the data
  • Creating the model
  • Model performance evaluation and cross-validation
  • Confusion matrix
  • K-fold cross-validation
  • Named entity recognition
  • Installing NER
  • Combining NER and sentiment analysis
  • Summary
  • Chapter 5: Campaigns and Consumer Reaction Analytics on YouTube - Structured and Unstructured
  • Scope and process
  • Getting the data
  • How to get a YouTube API key
  • Data pull
  • Data processing.
  • Data analysis
  • Sentiment analysis in time
  • Sentiment by weekday
  • Comments in time
  • Number of comments by weekday
  • Summary
  • Chapter 6: The Next Great Technology - Trends Mining on GitHub
  • Scope and process
  • Getting the data
  • Rate Limits
  • Connection to GitHub
  • Data pull
  • Data processing
  • Textual data
  • Numerical data
  • Data analysis
  • Top technologies
  • Programming languages
  • Programming languages used in top technologies
  • Top repositories by technology
  • Comparison of technologies in terms of forks, open issues, size, and watchers count
  • Forks versus open issues
  • Forks versus size
  • Forks versus watchers
  • Open issues versus Size
  • Open issues versus Watchers
  • Size versus watchers
  • Summary
  • Chapter 7: Scraping and Extracting Conversational Topics on Internet Forums
  • Scope and process
  • Getting the data
  • Introduction to scraping
  • Scrapy framework
  • How it works
  • Related tools
  • Creating a project
  • Creating spiders
  • Teamspeed forum spider
  • Data pull and pre-processing
  • Data cleaning
  • Part-of-speech extraction
  • Data analysis
  • Introduction to topic models
  • Latent Dirichlet Allocation
  • Applying LDA to forum conversations
  • Topic interpretation
  • Summary
  • Chapter 8: Demystifying Pinterest through Network Analysis of Users Interests
  • Scope and process
  • Getting the data
  • Pinterest API
  • Step 1 - creating an application and obtaining app ID and app secret
  • Step 2 - getting your authorization code (access code)
  • Step 3 - exchanging the access code for an access token
  • Step 4 - testing the connection
  • Getting Pinterest API data
  • Scraping Pinterest search results
  • Building a scraper with Selenium
  • Scraping time constraints
  • Data pull and pre-processing
  • Pinterest API data
  • Bigram extraction
  • Building a graph
  • Pinterest search results data.
  • Bigram extraction
  • Building a graph
  • Data analysis
  • Understanding relationships between our own topics
  • Finding influencers
  • Conclusions
  • Community structure
  • Summary
  • Chapter 9: Social Data Analytics at Scale - Spark and Amazon Web Services
  • Different scaling methods and platforms
  • Parallel computing
  • Distributed computing with Celery
  • Celery multiple node deployment
  • Distributed computing with Spark
  • Text mining With Spark
  • Topic models at scale
  • Spark on the Cloud - Amazon Elastic MapReduce
  • Summary
  • Index.

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