Analysing Tweets using R
What you'll learn
- Gathering Data from Twitter
- Using Twitter API
- Using Google Map API
- Analysing Twitter Data
- Lexicon based Emotion Analysis
- Use of many related R Libraries
Requirements
- Must have knowledge of Programming
- Must have knowledge of R Programming
- Must have knowledge of using RStudio
Description
People around the globe make over 500 million tweets per day. So, one can only imagine the sheer volume of data available with Twitter. This data is a treasure trove of information. However, one needs to know how to gather this data and then conduct the needed analysis.
This course provides all the information regarding
How to gather data from Twitter using R Programming
How to conduct basic analysis of the data gathered from Twitter
How to extract the Emotion expressed in the Tweets gathered
The course also discusses associated APIs required for analysing Twitter data like Google Maps API.
To take full advantage of the course, it will be required to create a developer account with Twitter. All the necessary steps for getting a Twitter Developer Account is provided in the course. However, it must be noted that it is the discretion of Twitter whether they will grant a Twitter Developer account against an application. Nevertheless, all the contents of the course can be followed and understood without a Twitter Developer account. Only difference will be that the data extracted from Twitter will be restricted. With limited data, the analysis possible will be limited.
We will use R Programming throughout this course. Thus, this course requires that the participants are conversant with R Programming.
If you prefer any other programming language (like. Python, etc.), then you can use this course to learn all the nuances of analysing Twitter Data and apply the same in programming in your language of your preference.
Who this course is for:
- Students
- Researchers
- Data Analysts
- Data Scientists
- Computer Software Programmers
Instructor
- 4.2 Instructor Rating
- 244 Reviews
- 7,286 Students
- 7 Courses
Partha started his career in 1989 as a programmer. In his first assignment, he was involved in development of a Cricket Tournament management system as a part of the team from Centre for Development of Telematics (C-DOT) requested by the Prime Minister of India, Mr. Rajiv Gandhi. Since then Partha has developed Tea Garden automation solution, Hospital Management solution, Travel Management solution, Manufacturing Resource Planning (MRP II) solution, Insurance Management solution and Tax automation solution (for Government of Thailand).
Partha got involved in Telecom solution with project from Total Access Communications, Bangkok in 1996. Partha developed the completed solution architecture and designed & developed the complete infrastructure services and primitives on top of which the end-to-end Customer Care and Billing solution was developed between 1996-1998.
Partha has worked for companies including Amdocs, Portal, Siemens and has developed key components of their solutions. For Siemens, Partha developed the complete BSS suite.
Partha worked with Mobily, Saudi Arabia as the Enterprise Architect and has first-hand of experience of work inside a Telecom Operator.
Partha started his own company, Majumdar Consultancy Pvt Ltd, in 2014. He partnered with a Dubai based businessman to open SI Solutions India Pvt Ltd in 2016. In 2019, he joined Tools and Solutions, Saudi Arabia as Director - Professional Services to establish the Professional Services business.
Partha has recently developed a Remote Control, which can be controlled from a Web Site. The Remote Control can in turn control any device. The Remote Control to be controlled needs having Infra-Red sensing capabilities. The Remote Control is controlled through DragonBoard 410C through a Android Program.
Partha has been working on fine tuning the algorithm for a Access Control System through Face Recognition. The program has been developed using Convolutional Neural Network (CNN).
Partha has also developed a software which tries to predict the Stock Market. The solution has been developed using Recurrent Neural Network (RNN). The solution presently predict with an accuracy of 77%.