Twitter Data Visualization Using R

Twitter Data Visualization Using R

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.

Overview

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 = ',')

Image for post

The variables of data

Data Visualization

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 **created**.

data-visualization sentiment-analysis data data-science rstats

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Exploratory Data Analysis is a significant part of Data Science

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.

What Are The Advantages and Disadvantages of Data Science?

Online Data Science Training in Noida at CETPA, best institute in India for Data Science Online Course and Certification. Call now at 9911417779 to avail 50% discount.

50 Data Science Jobs That Opened Just Last Week

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

Exploratory Data Analysis is a significant part of Data Science

You will discover Exploratory Data Analysis (EDA), the techniques and tactics that you can use, and why you should be performing EDA on your next problem.

Data Cleaning in R for Data Science

A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.