最新章節

• Summary
• Spark on the Cloud – Amazon Elastic MapReduce
• Topic models at scale
• Text mining With Spark
• Distributed computing with Spark
• Celery multiple node deployment

• Summary 更新時間：2021-07-15 17:25:34
• Spark on the Cloud – Amazon Elastic MapReduce
• Topic models at scale
• Text mining With Spark
• Distributed computing with Spark
• Celery multiple node deployment
• Distributed computing with Celery
• Parallel computing
• Different scaling methods and platforms
• Social Data Analytics at Scale – Spark and Amazon Web Services
• Summary
• Community structure
• Conclusions
• Finding influencers
• Understanding relationships between our own topics
• Data analysis
• Building a graph
• Bigram extraction
• Pinterest search results data
• Building a graph
• Bigram extraction
• Pinterest API data
• Data pull and pre-processing
• Scraping time constraints
• Building a scraper with Selenium
• Scraping Pinterest search results
• Getting Pinterest API data
• Step 4 - testing the connection
• Step 3 - exchanging the access code for an access token
• Step 2 - getting your authorization code (access code)
• Step 1 - creating an application and obtaining app ID and app secret
• Pinterest API
• Getting the data
• Scope and process
• Demystifying Pinterest through Network Analysis of Users Interests
• Summary
• Topic interpretation
• Applying LDA to forum conversations
• Latent Dirichlet Allocation
• Introduction to topic models
• Data analysis
• Part-of-speech extraction
• Data cleaning
• Data pull and pre-processing
• Teamspeed forum spider
• Creating spiders
• Creating a project
• Related tools
• How it works
• Scrapy framework
• Introduction to scraping
• Getting the data
• Scope and process
• Scraping and Extracting Conversational Topics on Internet Forums
• Summary
• Size versus watchers
• Open issues versus Watchers
• Open issues versus Size
• Forks versus watchers
• Forks versus size
• Forks versus open issues
• Comparison of technologies in terms of forks open issues size and watchers count
• Top repositories by technology
• Programming languages used in top technologies
• Programming languages
• Top technologies
• Data analysis
• Numerical data
• Textual data
• Data processing
• Data pull
• Connection to GitHub
• Rate Limits
• Getting the data
• Scope and process
• The Next Great Technology – Trends Mining on GitHub
• Summary
• Number of comments by weekday
• Comments in time
• Sentiment by weekday
• Sentiment analysis in time
• Data analysis
• Data processing
• Data pull
• How to get a YouTube API key
• Getting the data
• Scope and process
• Campaigns and Consumer Reaction Analytics on YouTube – Structured and Unstructured
• Summary
• Combining NER and sentiment analysis
• Installing NER
• Named entity recognition
• K-fold cross-validation
• Confusion matrix
• Model performance evaluation and cross-validation
• Creating the model
• Labeling the data
• Customized sentiment analysis
• Sentiment analysis
• Data cleaning
• Data pull
• Streaming API
• Rate Limits
• REST API Search endpoint
• Data extraction
• Getting Twitter API keys
• Getting the data
• Scope and process
• Analyzing Twitter Using Sentiment Analysis and Entity Recognition
• Summary
• How can brands benefit from it?
• Applying Alchemy API
• Setting up an application
• Connecting to the Alchemy API
• Introducing the Alchemy API
• How to extract emotions?
• Uncovering emotions
• Comments
• Brand posts
• Maximum likes
• User comments
• Brand posts
• Maximum shares
• Detecting trends in time series
• User comments
• Brand posts
• Noun phrases
• User hashtags
• Brand posts
• User keywords
• Extracting verbatims for keywords
• Keywords
• Step 4 – content analysis
• Step 3 – feature extraction
• Step 2 – data pull
• Step 1 – data extraction
• Analysis
• Data type
• Scope and process
• Project planning
• The Facebook API
• Facebook brand page
• Uncovering Brand Activity Popularity and Emotions on Facebook
• Summary
• MongoDB using Python
• Starting MongoDB
• Setting up the environment
• Installing MongoDB
• MongoDB to store and access social data
• Duplicate removal
• Pre-processing and text normalization
• Structure of data
• Data type and encoding
• Basic cleaning techniques
• Connecting to the API
• Selecting the endpoint
• Creating an application
• Pinterest
• Connecting to the API
• Selecting the endpoint
• Creating an application and obtaining an access token programmatically
• YouTube
• Connecting to the API
• Selecting the endpoint
• Obtaining OAuth tokens programmatically
• GitHub
• Connect to the API
• Selecting the endpoint
• Creating an app and getting an access token
• Facebook
• Using requests to connect
• Selecting the endpoint
• Creating application
• Twitter
• Parsing API outputs
• Practical usage of OAuth
• OAuth1 and OAuth2
• Connecting to social network platforms without OAuth
• Why do we need to use OAuth?
• Application authentication
• User authentication
• What is OAuth?
• Introduction to authentication techniques
• Connecting principles of APIs
• Limitations of social media APIs
• Advantages of social media APIs
• Stream API
• RESTful API
• Different types of API
• APIs in a nutshell
• Harnessing Social Data - Connecting Capturing and Cleaning
• Summary
• Getting started with the toolset
• Visualizing the data
• Setting up data structure libraries
• Techniques for social media analysis
• Brief introduction to machine learning
• Analyzing the data
• Scraping and crawling
• Defining API
• Getting the data
• Illustrating Git
• Selecting an IDE
• Defining Python
• Working environment
• Understanding the process
• Exploring social data applications
• Defining the semantic web
• Understanding semantics
• Delving into social data
• Platforms on platform
• Social impacts
• Notion of influence
• Introducing social graph
• Introduction to the Latest Social Media Landscape and Importance
• Questions
• Piracy
• Errata
• Downloading the example code
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Customer Feedback
• Why subscribe?
• www.PacktPub.com
• About the Reviewer
• Acknowledgments
• About the Authors
• Credits
• Python Social Media Analytics
• Copyright
• Title Page
• cover

• cover
• Title Page
• Copyright
• Python Social Media Analytics
• Credits
• About the Authors
• Acknowledgments
• About the Reviewer
• www.PacktPub.com
• Why subscribe?
• Customer Feedback
• Preface
• What this book covers
• What you need for this book
• Who this book is for
• Conventions
• Reader feedback
• Customer support
• Downloading the example code
• Errata
• Piracy
• Questions
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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 更新時間：2021-07-15 17:25:34