Twitter Data Analysis with R

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Text Mining – Package tm 4 ◮ removeNumbers, removePunctuation, removeWords, removeSparseTerms, stripWhitespace: remove numbers, punctuations, words or extra whitespaces ◮ removeSparseTerms: remove sparse terms from a term-document matrix ◮ stopwords: various kinds of stopwords ◮ stemDocument, stemCompletion: stem words and complete stems ◮ TermDocumentMatrix, DocumentTermMatrix: build a term-document matrix or a document-term matrix ◮ termFreq: generate a term frequency vector ◮ findFreqTerms, findAssocs: find frequent terms or associations of terms ◮ weightBin, weightTf, weightTfIdf, weightSMART, WeightFunction: various ways to weight a term-document matrix 4 https://cran.r-project.org/package=tm 34 / 40
Topic Modelling and Sentiment Analysis – Packages topicmodels & sentiment140 Package topicmodels 5 ◮ LDA: build a Latent Dirichlet Allocation (LDA) model ◮ CTM: build a Correlated Topic Model (CTM) model ◮ terms: extract the most likely terms for each topic ◮ topics: extract the most likely topics for each document Package sentiment140 6 ◮ sentiment: sentiment analysis with the sentiment140 API, tune to Twitter text analysis 5 https://cran.r-project.org/package=topicmodels 6 https://github.com/okugami79/sentiment140 35 / 40
Page 1 and 2: Twitter Data Analysis with R Yancha
Page 3 and 4: Twitter ◮ An online social networ
Page 5 and 6: Techniques and Tools ◮ Techniques
Page 7 and 8: Outline Introduction Tweets Analysi
Page 9 and 10: (n.tweet
Page 11 and 12: 1 http://stackoverflow.com/question
Page 13 and 14: Build Term Document Matrix tdm
Page 15 and 16: library(ggplot2) ggplot(df, aes(x=t
Page 17 and 18: data r mining slide big analysing p
Page 19 and 20: Network of Terms library(graph) lib
Page 21 and 22: Topic Modelling topics
Page 25 and 26: ●● ● ●● ● ● ● ●
Page 27 and 28: Top Retweeted Tweets # select top r
Page 29 and 30: Tracking Message Propagation tweets
Page 33: Twitter Data Extraction - Package t
Page 39 and 40: Online Resources ◮ RDataMining Re

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