Index
A
- active users
- used, for building heterogeneous dataset / Building a heterogeneous dataset using the most active users
- additional metrics
- building / Building additional metrics
- Anomaly/spam detection
- about / Steps for sentiment analysis
- API
- app
- creating, on Facebook platform / Creating an app on the Facebook platform
- creating, on GitHub / Creating an app on GitHub
- avatar
- about / Using the Tumblr API
B
- Betweenness / Betweenness
- Blogger
- about / Getting Blogger data
- Blogger API usage
- URL / Getting Blogger data
- Blogger data
- obtaining / Getting Blogger data
- URL / Getting Blogger data
- business cases
- implementing / Other business cases
- defining / Business case, Business cases
C
- celebrity users and location hashtags
- URL / Reference
- challenges, social media mining
- Big Data / Challenges for social media mining
- Sufficiency / Challenges for social media mining
- Noise removal error / Challenges for social media mining
- Evaluation dilemma / Challenges for social media mining
- closeness / Closeness
- cluster / Cluster
- clustering
- about / Clustering the pictures
- clustering analysis
- URL / Reference
- Community Detection
- via Clustering / Community detection via clustering
- correlation analysis, EDA
- defining / EDA – correlation analysis
- Watchers, related to Forks / How Watchers is related to Forks
- correlation, with regression line / Correlation with regression line
- correlation, with local regression curve / Correlation with local regression curve
- correlation, on segmented data / Correlation on segmented data
- correlation, between languages / Correlation between the languages that user's use to code
- trend of correlation, obtaining / How to get the trend of correlation?
- references / Reference
- CTR performance
- measuring, for page / Measuring CTR performance for a page
- customer relationship management (CRM)
- about / Social media and its importance
D
- data
- accessing, from R / Accessing data from R
- retrieving, from Wikipedia / Retrieving data from Wikipedia
- accessing, from Quora / Accessing data from Quora
- data access
- public media, searching with specific hashtag / Searching public media for a specific hashtag
- public media, searching from specific location / Searching public media from a specific location
- public media, extracting of user / Extracting public media of a user
- user profile, extracting / Extracting user profile
- followers, obtaining / Getting followers
- user, following / Who does the user follow?
- comments, obtaining / Getting comments
- hashtag, using / Number of times hashtag is used
- data processing
- about / Data processing
- dataset
- building / Building a dataset
- user profile / User profile
- user media / User media
- travel-related media / Travel-related media
- users, following / Who do they follow?
- data visualization R packages
- simple word cloud / The simple word cloud
- sentiment analysis Wordcloud / Sentiment analysis Wordcloud
- degree
- about / Degree
E
- EDA techniques
- univariate / Exploratory data analysis
- bivariate / Exploratory data analysis
- multivariate / Exploratory data analysis
- emotions, sentiment package
- anger / Estimating sentiment (B)
- disgust / Estimating sentiment (B)
- fear / Estimating sentiment (B)
- joy / Estimating sentiment (B)
- sadness / Estimating sentiment (B)
- surprise / Estimating sentiment (B)
- entities, tweet
- Handle / Twitter vocabulary
- Hashtags / Twitter vocabulary
- URL / Twitter vocabulary
- exploratory data analysis
- defining / Exploratory data analysis
F
- Facebook app
- Facebook Graph Search
- about / Graph mining
- Facebook page data
- obtaining / Getting Facebook page data
- Facebook platform
- app, creating / Creating an app on the Facebook platform
- Facebook Query Language
- Foursquare
- venue data, retrieving from / Retrieving venue data from Foursquare
- about / Retrieving venue data from Foursquare
- URL / Retrieving venue data from Foursquare
- use cases / Use cases
G
- GitHub
- app, creating / Creating an app on GitHub
- URL / Accessing GitHub data from R
- GitHub app
- GitHub data
- accessing, from R / Accessing GitHub data from R
- GitHub package
- installing / GitHub package installation and authentication
- authentication / GitHub package installation and authentication
- GitHub package, R
- references / Reference
- Google Maps
- used, for mapping solutions / Mapping solutions using Google Maps
- URL / Mapping solutions using Google Maps
- graphical analysis, EDA
- defining / EDA – graphical analysis
- language used, among active GitHub users / Which language is most popular among the active GitHub users?
- distribution of watchers, in GitHub / What is the distribution of watchers, forks, and issues in GitHub?
- distribution of forks, in GitHub / What is the distribution of watchers, forks, and issues in GitHub?
- distribution of issues, in GitHub / What is the distribution of watchers, forks, and issues in GitHub?
- repositories, with issues / How many repositories had issues?
- repositories, updating / What is the trend on updating repositories?
- users, comparing through heat map / Compare users through heat map
- graph mining / Graph mining
H
- heterogeneous dataset
- building, active users used / Building a heterogeneous dataset using the most active users
- HTML DOM
I
- igraph
- about / Social network analysis
- influencers
- about / Influencers
- based, on single post / Based on a single post
- based, on multiple posts / Based on multiple posts
- Instagram account
- Instagram platform
- app, creating / Creating an app on the Instagram platform
- instaR package
- installing / Installation and authentication of the instaR package
- authentication / Installation and authentication of the instaR package
- URL / Reference
- Item-to-Item Collaborative Filtering, Amazon
- URL / Reference
L
- LinkedIn
- professional network data, defining from / Professional network data from LinkedIn
- about / Professional network data from LinkedIn
- LinkedIn app
M
- methods
- used, for visualizing data / Result visualization
- mining algorithms
- defining / Data modeling – the application of mining algorithms
- opinion mining (sentiment analysis) / Opinion mining (sentiment analysis)
- most popular destination
- finding / Finding the most popular destination
- locations / Locations
- locations, with most likes / Locations with most likes
- locations, most talked about / Locations most talked about
- people, talking about locations / What are people saying about these locations?
- repeating locations / Most repeating locations
- multiple newsfeeds
- updating / The order of stories on a user's home page
N
- Naive Bayes
- about / Estimating sentiment (B)
- network analysis
- defining / A basic analysis of your network, Network analysis and visualization
- social network analysis / Social network analysis
- degree / Degree
- Betweenness / Betweenness
- closeness / Closeness
- cluster / Cluster
- communities / Communities
- network visualization
- defining / Network analysis and visualization
- Neural Networks (NN)
- about / Steps for sentiment analysis
- new app, Twitter
- URL / Creating a new app
O
- OAuth
- URL / Getting authentication from the social website – OAuth 2.0
- and Oauth2.0, comparing / Differences between OAuth and OAuth 2.0
- OAuth 2.0
- Oauth2.0
- and OAuth, comparing / Differences between OAuth and OAuth 2.0
- online courses
- URL / Reference
- online courses, on EDA
- references / Reference
P
- part-of-speech tagging (pos)
- about / Steps for sentiment analysis
- partnership program
- pictures
- clustering / Clustering the pictures
- pie chart
- popular personalities
- defining / Popular personalities
- users, with most number of followers / Who has the most followers?
- user, who follows most number of people / Who follows more people?
- active users / Who shared most media?
- overall top users / Overall top users
- viral media, finding / Most viral media
- product reviews
- accessing, from sites / Accessing product reviews from sites
Q
- quintuple
- about / Steps for sentiment analysis
- Quora
- data, accessing from / Accessing data from Quora
- about / Accessing data from Quora
- references / Accessing data from Quora
R
- R
- preprocessing / Preprocessing and cleaning in R
- cleaning / Preprocessing and cleaning in R
- data, accessing from / Accessing data from R
- GitHub, accessing from / Accessing GitHub data from R
- rbind function
- read.csv
- recommendation, to users
- providing / Recommendations to the users
- implementing / How to do it
- top three recommendations / Top three recommendations
- recommendations to friends
- defining / Recommendations to friends
- output, reading / Reading the output
- recommendation system
- improvements / Improvements to the recommendation system
- repos
- result visualization
- about / Result visualization
- return-of-investments (ROIs)
- retweets (RTs)
- about / Cleaning the corpus
- Rfacebook package
- defining / Rfacebook package installation and authentication
- installation / Installation
- working / A closer look at how the package works
- RgoogleMaps package
S
- sentiment (A)
- examples / Estimating sentiment (A)
- sentiment analysis
- sentiment analysis Wordcloud
- Classify_emotion / Sentiment analysis Wordcloud
- Classify_polarity / Sentiment analysis Wordcloud
- sentiment orientation (SO)
- about / Steps for sentiment analysis
- sentiment package
- defining / Estimating sentiment (B)
- sites
- product reviews, accessing from / Accessing product reviews from sites
- social media
- defining / Social media and its importance
- platforms / Various social media platforms
- searching on / Searching on social media
- social media data
- references / Sentiment analysis Wordcloud
- social media mining
- about / Social media mining
- challenges / Challenges for social media mining
- techniques / Social media mining techniques
- process / The generic process of social media mining
- authentication, obtaining from social website / Getting authentication from the social website – OAuth 2.0
- data visualization R packages / Data visualization R packages
- example / An example of social media mining
- social media mining (SMM)
- about / Social media mining
- Social Network Analysis
- URL / Degree
- social network analysis
- using / Communities
- social spammer detection
- about / Steps for sentiment analysis
- solutions
- mapping, Google Maps used / Mapping solutions using Google Maps
- spam detection
- about / Spam detection
- spam detection algorithm
- implementing / Implementing a spam detection algorithm
- supervised machine learning algorithms
- about / Steps for sentiment analysis
- Support Vector Machine (SVM)
- about / Steps for sentiment analysis
T
- techniques, social media mining
- graph mining / Graph mining
- text mining / Text mining
- temporary token
- URL / Installation
- text mining / Text mining
- timeline
- about / Twitter vocabulary
- Timeline
- about / Twitter vocabulary
- top contributors
- trending topics
- defining / Trending topics
- trend analysis / Trend analysis
- Tumblr
- URL / Using the Tumblr API
- references / Using the Tumblr API
- Tumblr API
- using / Using the Tumblr API
- URL / Using the Tumblr API
- tweets
- about / Twitter vocabulary
- constraints / Searching tweets
- Twitter
- defining / Twitter and its importance
- about / Twitter vocabulary
- URL / Creating a new app
- Twitter API connection
- creating / Creating a Twitter API connection
- new app, creating / Creating a new app
- trending topics, finding / Finding trending topics
- tweets, searching / Searching tweets
- Twitter APIs
- about / Understanding Twitter's APIs
- Twitter vocabulary / Twitter vocabulary
- Twitter app
- Twitter sentiment analysis
- defining / Twitter sentiment analysis
- tweets, collecting as corpus / Collecting tweets as a corpus
- corpus, cleaning / Cleaning the corpus
- sentiment (A), estimating / Estimating sentiment (A)
- sentiment (A), sample results / Estimating sentiment (A)
- sentiment (B), estimating / Estimating sentiment (B)
- Twitter stream
- about / Twitter vocabulary
- Twitter timeline
- about / Twitter vocabulary
- Twitter vocabulary
- defining / Twitter vocabulary
W
- Wikipedia
- data, retrieving from / Retrieving data from Wikipedia
Y
- Yelp
- about / Yelp and other networks
- URL / Yelp and other networks
- limitations / Limitations