最新章節(jié)
- Summary
- Spark on the Cloud – Amazon Elastic MapReduce
- Topic models at scale
- Text mining With Spark
- Distributed computing with Spark
- Celery multiple node deployment
品牌：中圖公司
上架時(shí)間：2021-07-15 17:00:03
出版社：Packt Publishing
本書數(shù)字版權(quán)由中圖公司提供，并由其授權(quán)上海閱文信息技術(shù)有限公司制作發(fā)行
- Summary 更新時(shí)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
- 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
- Using requests to connect
- Selecting the endpoint
- Creating application
- 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
- Creating application
- Selecting the endpoint
- Using requests to connect
- 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
- 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(shí)間：2021-07-15 17:25:34
