fied misspelling of calculate

DESCRIPTION

@@ -1,10 +1,10 @@
 Package: sentimentr
-Title: Calulate Text Polarity Sentiment
+Title: Calculate Text Polarity Sentiment
 Version: 0.1.0
 Authors@R: c(person("Tyler", "Rinker", email =
         "tyler.rinker@gmail.com", role = c("aut", "cre")))
 Maintainer: Tyler Rinker <tyler.rinker@gmail.com>
-Description: Calulate text polarity sentiment at the sentence level and
+Description: Calculate text polarity sentiment at the sentence level and
         optionally aggregate by rows or grouping variable(s).
 Depends: R (>= 3.2.1)
 Suggests: testthat

R/sentimentr-package.R

@@ -1,6 +1,6 @@
-#' Calulate Text Polarity Sentiment
+#' Calculate Text Polarity Sentiment
 #'
-#' Calulate text polarity sentiment at the sentence level and optionally
+#' Calculate text polarity sentiment at the sentence level and optionally
 #' aggregate by rows or grouping variable(s).
 #' @docType package
 #' @name sentimentr

README.Rmd

@@ -34,7 +34,7 @@ knitr::opts_chunk$set(fig.path = "inst/figure/")
 
 <img src="inst/sentimentr_logo/r_sentimentr.png" width="150" alt="readability Logo">  
 
-**sentimentr** is designed to quickly calulate text polarity sentiment at the sentence level and optionally aggregate by rows or grouping variable(s).
+**sentimentr** is designed to quickly calculate text polarity sentiment at the sentence level and optionally aggregate by rows or grouping variable(s).
 
 **sentimentr** is a response to my own needs with sentiment detection that were not addressed by the current **R** tools.  My own `polarity` function in the **qdap** package is slower on larger data sets.  It is a dictionary lookup approach that tries to incorporate weighting for valence shifters (negation and amplifiers/deamplifiers).  Matthew Jocker's created the [**syuzhet** ](http://www.matthewjockers.net/2015/02/02/syuzhet/) package that utilizes dictionary lookups for the Bing, NRC, and Afinn methods.  He also utilizes a wrapper for the [Stanford coreNLP](http://nlp.stanford.edu/software/corenlp.shtml) which uses much more sophisticated analysis.  Jocker's dictionary methods are fast but are more prone to error in the case of valence shifters.  Jocker's [addressed these critiques](http://www.matthewjockers.net/2015/03/04/some-thoughts-on-annies-thoughts-about-syuzhet/) explaining that the method is good with regard to analyzing general sentiment in a piece of literature.  He points to the accuracy of the Stanford detection as well.  In my own work I need better accuracy than a simple dictionary lookup; something that considers valence shifters yet optimizes speed which the Stanford's parser does not.  This leads to a trade off of speed vs. accuracy.  The equation below describes the dictionary method of **sentimentr** that may give better results than a dictionary approach that does not consider valence shifters but will likely still be less accurate than Stanford's approach.  Simply, **sentimentr** attempts to balance accuracy and speed.
 

README.md

@@ -14,7 +14,7 @@ Status](https://coveralls.io/repos/trinker/sentimentr/badge.svg?branch=master)](
 </p>
 <img src="inst/sentimentr_logo/r_sentimentr.png" width="150" alt="readability Logo">
 
-**sentimentr** is designed to quickly calulate text polarity sentiment
+**sentimentr** is designed to quickly calculate text polarity sentiment
 at the sentence level and optionally aggregate by rows or grouping
 variable(s).
 
@@ -402,19 +402,19 @@ see that Stanford takes the longest time while **sentimentr** and
 
     Unit: milliseconds
                        expr        min         lq       mean     median
-                 stanford() 18296.7227 19404.6873 19877.0087 20512.6518
-        sentimentr_hu_liu()   173.6543   203.8537   214.1287   234.0531
-     sentimentr_sentiword()   738.5330   859.8716   907.5855   981.2101
-             syuzhet_binn()   282.7214   315.9404   331.5220   349.1594
-              syuzhet_nrc()   630.9808   759.2502   814.1419   887.5196
-            syuzhet_afinn()   166.2193   166.4287   169.4098   166.6381
+                 stanford() 19534.8874 19719.3494 19782.8247 19903.8114
+        sentimentr_hu_liu()   220.7847   224.1138   226.0406   227.4429
+     sentimentr_sentiword()   969.6914   973.4066   979.2458   977.1219
+             syuzhet_binn()   356.9010   357.5310   363.1912   358.1610
+              syuzhet_nrc()   884.7328   892.4310   914.2375   900.1292
+            syuzhet_afinn()   162.0473   162.6307   172.1710   163.2141
              uq        max neval
-     20667.1517 20821.6515     3
-       234.3660   234.6788     3
-       992.1118  1003.0134     3
-       355.9223   362.6852     3
-       905.7224   923.9253     3
-       171.0051   175.3720     3
+     19906.7934 19909.7754     3
+       228.6686   229.8943     3
+       984.0230   990.9240     3
+       366.3362   374.5115     3
+       928.9898   957.8504     3
+       177.2328   191.2516     3
 
 Contact
 =======

inst/CITATION

@@ -2,15 +2,15 @@ citHeader("To cite sentimentr in publications, please use:")
 
 
 citEntry(entry = "manual",
-    title = "{sentimentr}: Calulate Text Polarity Sentiment",
+    title = "{sentimentr}: Calculate Text Polarity Sentiment",
     author = "Tyler W. Rinker",
     organization = "University at Buffalo/SUNY",
     address = "Buffalo, New York",
     note = "version 0.1.0",
     year = "2015",
     url = "http://github.com/trinker/sentimentr",
     textVersion  = paste("Rinker, T. W. (2015).",
-        "sentimentr: Calulate Text Polarity Sentiment",
+        "sentimentr: Calculate Text Polarity Sentiment",
         "version 0.1.0. University at Buffalo. Buffalo, New York.",
         "http://github.com/trinker/sentimentr")
 )

man/sentimentr.Rd

@@ -5,9 +5,9 @@
 \alias{package-sentiment}
 \alias{sentimentr}
 \alias{sentimentr-package}
-\title{Calulate Text Polarity Sentiment}
+\title{Calculate Text Polarity Sentiment}
 \description{
-Calulate text polarity sentiment at the sentence level and optionally
+Calculate text polarity sentiment at the sentence level and optionally
 aggregate by rows or grouping variable(s).
 }