Twitter Data Visualization Using R. In this post I want to present a small case study where I analyze Twitter text data. Data exploration aims to get any information and insight from Twitter data.
For this project, we use primary data of Twitter by crawling on 28th — 29th of May 2019. Further, the data is in CSV format (comma delimited) and it can be downloaded HERE. It refers to two topics, the data of Joko Widodo which contains the keyword “Joko Widodo” and the other one is the data of Prabowo Subianto that has the keyword “Prabowo Subianto”. These include several variables and information in order to determine user sentiment. Actually, the data has 16 variables or attributes and more than 1000 observations (for both data). Some variables are listed in Table 1.
## Import libraries library(ggplot2) library(lubridate) ## Load the data of Joko Widodo data.jokowi.df = read.csv(file = 'data-joko-widodo.csv', header = TRUE, sep = ',') senti.jokowi = read.csv(file = 'sentiment-joko-widodo.csv', header = TRUE, sep = ',') ## Load the data of Prabowo Subianto data.prabowo.df = read.csv(file = 'data-prabowo-subianto.csv', header = TRUE, sep = ',') senti.prabowo = read.csv(file = 'sentiment-prabowo-subianto.csv', header = TRUE, sep = ',')
The variables of data
Data exploration aims to get any information and insight from Twitter data. It should be noted that the data had been conducted text pre-processing. Exploration is carried out for variables which are considered quite interesting to be discussed. For instance, the variable
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.
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