Index
A
- Amazon Web Services (AWS.tools) / Why R?
- Anscombe's quartet / Visualization in R
- application programming interface (API) / Obtaining Twitter data
- arguments
- about / Functions, arguments, and help
- assignment operator
- about / Functions, arguments, and help
B
- bccmpls / Preliminary analyses
- benefits, R / Why R?
- Big Data
- about / What is Big Data?
- scope / What is Big Data?
C
- carrot (>) operator / The basics – assignment and arithmetic
- case studies, social media mining
- considerations / Introductory considerations
- lexicon based sentiment / Case study 1 – supervised social media mining – lexicon-based sentiment
- Naive Bayes classifier / Case study 2 – Naive Bayes classifier
- IRT models / Case study 3 – IRT models for unsupervised sentiment scaling
- corpus / Preliminary analyses
D
- data frames
- data mining
- dendrogram / Preliminary analyses
- Distributed Storage and List (dsl) / Why R?
- dist variable / Visualization in R
- document-term matrix
- building / Preliminary analyses
- Dropbox / Preliminary analyses
F
- Facebook
- about / The state of communication
- FAQs, R / Why R?
- files
- function
- about / Functions, arguments, and help
G
- Git / Preliminary analyses
- GitHub / Preliminary analyses
- GNU (GNU's Not Unix) / Why R?
- GoogleDrive / Preliminary analyses
- Google Reviews / Scherer's typology of emotions
- graphical user interface (GUI) / Quick start
H
- HadoopInteractiVE (hive) / Why R?
- Hadoop Steaming (HadoopSteaming) / Why R?
- help function
- about / Functions, arguments, and help
- hierarchical agglomerative clustering / Preliminary analyses
- honest signals
- about / Human sensors and honest signals
- human sensors
- about / Human sensors and honest signals
I
- information bounce
- about / The state of communication
- installation, R / Quick start
- integrated development environment (IDE) / Quick start
- Internet Movie Database (IMDB) / Sentiment polarity – data and classification
- IRT models / Unsupervised social media mining – Item Response Theory for text scaling
- IRT models case study / Case study 3 – IRT models for unsupervised sentiment scaling
- Item Response Theory (IRT)
L
- lexicon-based sentiment classification / Supervised social media mining – lexicon-based sentiment
- lexicon based sentiment case study / Case study 1 – supervised social media mining – lexicon-based sentiment
- lisarosie / Preliminary analyses
- logical operators
- about / Functions, arguments, and help
M
- modifiable areal unit problem (MAUP) / Measurement and inferential challenges
N
- naive Bayes classifier
- Naive Bayes classifier case study / Case study 2 – Naive Bayes classifier
- natural language processing (NLP)
- nontraditional social data
- versus traditional social data / Traditional versus nontraditional social data
O
- OAuthFactory function / Obtaining Twitter data
- operators, R
- arithmetic / The basics – assignment and arithmetic
- assignment / Functions, arguments, and help
- logical / Functions, arguments, and help
- opinion mining
- about / Opinion mining made difficult
- ordinary least squares (OLS) / A quick example – creating data frames and importing files
P
- plot() function / Visualization in R
- preliminary analyses
- about / Preliminary analyses
- ProjectTemplate
- URL / Preliminary analyses
Q
- qualitative approaches
- using / Quantitative approaches
R
- R
- benefits / Why R?
- FAQs / Why R?
- consequences / Why R?
- community / Why R?
- installing / Quick start
- URL, for FAQs / Quick start
- additional resources / Additional resources
- R code
- writing, tips / Style and workflow
- URL, for info / Style and workflow
- references / Further reading
- registerTwitterOAuth function / Obtaining Twitter data
- RStudio
- about / Why R?, Quick start
- URL / Quick start
S
- Scherers typology of emotions
- about / Scherer's typology of emotions
- emotion / Scherer's typology of emotions
- mood / Scherer's typology of emotions
- interpersonal stance / Scherer's typology of emotions
- attitudes / Scherer's typology of emotions
- personality traits / Scherer's typology of emotions
- sentiment
- about / Understanding sentiments
- sentiment analysis / An expanding field
- sentiment polarity
- measuring / Sentiment polarity – data and classification
- seq() function
- sequences
- social media
- sentiments, measuring / Sentiment and its measurement
- social media data
- potentials / Opinion mining made difficult
- pitfalls / Opinion mining made difficult
- overview / The nature of social media data
- inferential challenges / Measurement and inferential challenges
- measurements / Measurement and inferential challenges
- social media mining
- with sentiment analysis / Social media mining using sentiment analysis
- URL, for code / Social media mining using sentiment analysis
- about / Key concepts of social media mining
- concepts / Key concepts of social media mining
- content, identifying / Sentiment polarity – data and classification
- content, retrieving / Sentiment polarity – data and classification
- lexicon-based sentiment / Supervised social media mining – lexicon-based sentiment
- naive Bayes classifiers / Supervised social media mining – Naive Bayes classifiers
- Stack Overflow / Why R?
- state of communication section / The state of communication
- stop words / Preliminary analyses
T
- Text Mining Distributed Corpus Plug-In (tm.plug.dc) / Why R?
- traditional social data
- versus nontraditional social data / Traditional versus nontraditional social data
- traditional social science data
- versus, social media data / Good data versus bad data
- tweet / Scherer's typology of emotions
- tweets
- about / Preliminary analyses
- Twitter
- about / What is Big Data?, Why Twitter data?
- URL, for developer account / Obtaining Twitter data
- Twitter data
- need for / Why Twitter data?
- obtaining / Obtaining Twitter data
- about / Opinion mining made difficult
- twitteR package / Obtaining Twitter data
- Twittersphere / Scherer's typology of emotions
V
- vectors
- visualization
- about / Visualization in R
W
- weak ties
- about / Why Twitter data?
- WordCloud package / Preliminary analyses
- World Wide Web (WWW)
- about / The state of communication