Introduction to Text Mining with R using packages

Introduction to Text Mining with R using packages

  IJCTT-book-cover
 		 International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-54 Number-2
Year of Publication : 2017
Authors : Venkateswarlu pynam, Kolli srikanth, Ashok Surgala, Aravind Bammidi
DOI :  10.14445/22312803/IJCTT-V54P118

MLA

Venkateswarlu pynam, Kolli srikanth, Ashok Surgala, Aravind Bammidi "Introduction to Text Mining with R using packages". International Journal of Computer Trends and Technology (IJCTT) V54(2):116-119, December 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract
The fact that R is a language may deter some users who think “I can`t program". This should not be the case for two reasons. First R is an interpreted language, not a compiled one meaning that all commands typed on the keyboard are directly executed without requiring tobuild a complete program like in most computer languages (C, FORTRAN, Pascal . . .). R`s syntax is very simple and intuitive For instance a line a regression can be done with the command lm(y ~ x) which means “fitting a linear model with y as response and x as predictor". In R in order to be executed a function always needs to be written with parentheses even if there is nothing within them (e.g., ls()).R can be considered as a different implementation of S and is much used in as an educational language and research tool. The main advantages of R are the fact that R is freeware and that there is a lot of help available online. It is quite similar to other programming packages such as Mat Lab, but more user-friendly than programming languages such as C++ or FORTRAN and python etc.

References
1. Feinerer, I and Hornik, K. (2015). tm: Text MiningPackage,Rpackage version0.6-2. Retrieved from http://CRAN.R-project.org/ package=tm.
2. Meyer, D. and Hornik, K. and Feinerer, I. (2008). Text Mining Infrastructure in R. Journal of Statistical Software, 25 (5). pp. 1-54.
3. R Core Team. (2016). R: A language and environment for statistical computing. Retrieved from https://www.r-project.org/about.html.
4. https://cran.r-project.org/web/packages/text-mineR /vignettes/tm.pdf.
5. https://www.google.co.in/search?q=text+mining+in+r&safe=active&ei=Y6xIWsCAJYmAvQSr8prQAw&start=10&sa=N&biw=1093&bih=510.
6. https://www.hackerearth.com/practice/machine-learning/advanced-techniques/text-mining-feature-engineering-r/tutorial/.

Keywords
R, S, programming packages, text mining.