Disclaimer: If you sign up for a (paid) course using this link, R-exercises earns a commission. It does not impact what you pay for a course, and helps us to keep R-exercises free.Course Description
During this course you will be introduced to one of the most important and fast catching up data mining concept. The need for making sense of unstructured data and the knowledge of the various tools is of paramount importance.
What are the requirements?
- Download R & RStudio before starting this tutorial
- Download datasets folder in zipfile which is uploaded in session 1
- While it is not an essential prerequisite, it will be a good idea to go through our course on “Data Mining – Clustering Segmentation Using R, Tableau before going through this course
What am I going to get from this course?
- Perform text mining applications using structured & unstructured data
- Understand about document term matrix, term frequency, term frequency inverse document, term frequency for normalizing
- Differentiate between size of word which indicates the frequency of the said word in a word cloud, clustering based on related use for better insights and how to read the results in context to make sense of the word
- Understand from a practical case study the various steps of text mining in R and the use of Positive and negative word banks
- Learn Web and Social media extraction using R, Risk sensing – sentiment analysis, Twitter application management for extracting tweets
- Understand the clustering concept, that is an integral part of text mining
Who is the target audience?
- All the IT professionals, whose experience ranges from ‘0’ onwards are eligible to take this session. Especially professionals from data analysis, data warehouse, data mining, business intelligence, reporting, data science, etc, will naturally fit in well to take this course.