Twitter Text Mining

• Making a wordcloud for for most common used words in a person’s tweet.

• Load Twitter package, along with other necessary libraries.

#Instead of the traditional single package installation, we concactenate them into single code.
#install.packages(c("rtweet", #twitter package
     #              "tidytext", #text mining library
   #                "igraph", #plotting
     #              "ggraph")) #also plotting

• Load the installed and / required packages

#Concactenate the ncessary packages as well
lapply(c("rtweet", "tidytext", "igraph", "ggraph", "ggplot2", "dpylr"), require, character.only = TRUE)

## Loading required package: rtweet

## Warning: package 'rtweet' was built under R version 4.0.5

## Loading required package: tidytext

## Warning: package 'tidytext' was built under R version 4.0.5

## Loading required package: igraph

## Warning: package 'igraph' was built under R version 4.0.5

## 
## Attaching package: 'igraph'

## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum

## The following object is masked from 'package:base':
## 
##     union

## Loading required package: ggraph

## Warning: package 'ggraph' was built under R version 4.0.5

## Loading required package: ggplot2

## Loading required package: dpylr

## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'dpylr'

## [[1]]
## [1] TRUE
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## [1] TRUE
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## [[3]]
## [1] TRUE
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## [[4]]
## [1] TRUE
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## [1] TRUE
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## [1] FALSE

#climateTweets <- search_tweets2(q = "Climatechange", n = 10000, lang = "en", include_rts = FALSE)

It was at this moment that I realised that this project wasn’t going to work because I did not have a twitter account or any twitter presence. 😞😟

Off to another project, webscrapping 😃