R project(Analyze Twitter with R)
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The document provides instructions for using the R programming environment with the Twitter API. It details the setup process including signing up for Twitter, obtaining API credentials, and using libraries for text mining and word cloud visualization. Several demos are presented showcasing text mining techniques and trend analysis using Twitter data.
![Demo 3: Getting Trends
Trends <- availableTrendLocations()
head(trend)
Trends
World<-getTrends()
length(intersect(World[[1]], l[[1]]))
mat=matrix(nrow = 2 ,ncol = 2)
mat[[1,1]]=length(intersect(World[[1]], World[[1]]))
mat[[1,2]]=length(intersect(World[[1]], l[[1]]))
mat[[2,1]]=length(intersect(l[[1]], World[[1]]))
mat[[2,2]]=length(intersect(l[[1]], l[[1]]))](https://image.slidesharecdn.com/rproject-171224061243/85/R-project-Analyze-Twitter-with-R-12-320.jpg)
R project(Analyze Twitter with R)
- 2. Requirements Library: twitteR o Provides an interface to the Twitter web API. R is an open source programming language and software environment for statistical computing and graphics RStudio:RStudio is a free and open source integrated development environment (IDE) for R
- 3. Steps 1. Signup in twitter 2. Go to apps.twitter.com and register Application Program Interface(API) using twitter account 3. Get API Key, API Secret, Access Token, Access Token Secret 1. API KEY:JfpQuWXgr9YVhi1TeYzGLagKy 2. API Secret: Ao8afu033PQMslxsLvKMdgVhIFRMiV9Ie2ppXg3oB eIvF0ZnZz 3. Access Token: 927320525285634049- dOhOiQFH708QVJeslJfqVnYgKxxe5mu 4. Access Token Secret: JYQKFhsMjlI8xid8ozVerRfHSq8KrpZFky5m9vFh3ox
- 4. 4. Install Twitter Library 1. install. packages("twitteR") or 5. Set twitteR library 1. Load: library(twitteR) 2. setup_twitter_oauth(api_key,api_secret,access_toke n,access_token_secret)
- 5. Demo1: Text Mining with WordCloud install. Packages(“tm”) install. packages(“wordcloud”) library(“twitteR”) library(“tm”) library(“ROAuth”) library(“wordcloud”)
- 6. >ref<-searchTwitter(“Himalayas”, n=100,lang=“en”) >ref_samp<-sapply(ref, function(x), x$getText()) ref_corpus(vectorsource(ref_samp)) inspect(ref_corpus) ref_samp<-tm_map(ref_corpus, removepunctuation) ref_samp<-tm_map(ref_samp, content_transformer(tolower)) ref_samp<-tm_map(ref_samp, removewords, stopwards((“english”))
- 8. Demo2:Text Mining with R – an Analysis of Twitter Data >unstructured data >text categorization text clustering entity extraction Text Mining process extract data from Twitter clean extracted data and build a document-term matrix find frequent words and associations create a word cloud to visualize important words text clustering
- 9. Retrieve Tweets searchTwitter("hello World", since='2017-11-01', until='2017-11-8') jalandhar<- searchTwitter("thursday",geocode='31.326,75.5762,5 mi') japan<- searchTwitter("thursday",geocode='35.6895,139.691 7,5mi') a<-c(length(j),length(z))
- 12. Demo 3: Getting Trends Trends <- availableTrendLocations() head(trend) Trends World<-getTrends() length(intersect(World[[1]], l[[1]])) mat=matrix(nrow = 2 ,ncol = 2) mat[[1,1]]=length(intersect(World[[1]], World[[1]])) mat[[1,2]]=length(intersect(World[[1]], l[[1]])) mat[[2,1]]=length(intersect(l[[1]], World[[1]])) mat[[2,2]]=length(intersect(l[[1]], l[[1]]))
- 15. Thank You