Task 0: Understanding the Problem
Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. The goal of the Data Science Specialization Capstone Project is to create a predictive text algorithm in R that is based on a user’s text input. The algorithm will predict what word is most likely to be entered next.
Task 1: Data Acquisition and Cleaning
Download Data and Import Datasets
#Specify the source and destination of the download
destination_file <- "Coursera-SwiftKey.zip"
source_file <- "http://d396qusza40orc.cloudfront.net/dsscapstone/dataset/Coursera-SwiftKey.zip"
# execute the download
download.file(source_file, destination_file)
# extract the files from the zip file
unzip(destination_file)
library(stringi)
# inspect the data
list.files("final")
## [1] "de_DE" "en_US" "fi_FI" "ru_RU"
list.files("final/en_US")
## [1] "en_US.blogs.txt" "en_US.news.txt" "en_US.twitter.txt"
# import the blogs and twitter datasets in text mode
news <- readLines("final/en_US/en_US.news.txt", encoding = "UTF-8")
twitter <- readLines("final/en_US/en_US.twitter.txt", encoding = "UTF-8")
blogs <- readLines("final/en_US/en_US.blogs.txt", encoding = "UTF-8")
Summary Statistics for twitter, blogs, and news datasets
1. Twitter
#Number of lines in the file
system("wc -l final/en_US/en_US.twitter.txt")
#Preview of data layout (first 5 lines)
readLines(file("final/en_US/en_US.twitter.txt","r"), 5)
## [1] "How are you? Btw thanks for the RT. You gonna be in DC anytime soon? Love to see you. Been way, way too long."
## [2] "When you meet someone special... you'll know. Your heart will beat more rapidly and you'll smile for no reason."
## [3] "they've decided its more fun if I don't."
## [4] "So Tired D; Played Lazer Tag & Ran A LOT D; Ughh Going To Sleep Like In 5 Minutes ;)"
## [5] "Words from a complete stranger! Made my birthday even better :)"
2. Blogs
#Number of lines in the file
system("wc -l final/en_US/en_US.blogs.txt")
#Preview of data layout (first five lines)
readLines(file("final/en_US/en_US.blogs.txt","r"), 5)
## [1] "In the years thereafter, most of the Oil fields and platforms were named after pagan “gods”."
## [2] "We love you Mr. Brown."
## [3] "Chad has been awesome with the kids and holding down the fort while I work later than usual! The kids have been busy together playing Skylander on the XBox together, after Kyan cashed in his $$$ from his piggy bank. He wanted that game so bad and used his gift card from his birthday he has been saving and the money to get it (he never taps into that thing either, that is how we know he wanted it so bad). We made him count all of his money to make sure that he had enough! It was very cute to watch his reaction when he realized he did! He also does a very good job of letting Lola feel like she is playing too, by letting her switch out the characters! She loves it almost as much as him."
## [4] "so anyways, i am going to share some home decor inspiration that i have been storing in my folder on the puter. i have all these amazing images stored away ready to come to life when we get our home."
## [5] "With graduation season right around the corner, Nancy has whipped up a fun set to help you out with not only your graduation cards and gifts, but any occasion that brings on a change in one's life. I stamped the images in Memento Tuxedo Black and cut them out with circle Nestabilities. I embossed the kraft and red cardstock with TE's new Stars Impressions Plate, which is double sided and gives you 2 fantastic patterns. You can see how to use the Impressions Plates in this tutorial Taylor created. Just one pass through your die cut machine using the Embossing Pad Kit is all you need to do - super easy!"
3. News
#Number of lines in the file
system("wc -l final/en_US/en_US.news.txt")
#Preview of data layout (first five lines)
readLines(file("final/en_US/en_US.news.txt","r"), 5)
## [1] "He wasn't home alone, apparently."
## [2] "The St. Louis plant had to close. It would die of old age. Workers had been making cars there since the onset of mass automotive production in the 1920s."
## [3] "WSU's plans quickly became a hot topic on local online sites. Though most people applauded plans for the new biomedical center, many deplored the potential loss of the building."
## [4] "The Alaimo Group of Mount Holly was up for a contract last fall to evaluate and suggest improvements to Trenton Water Works. But campaign finance records released this week show the two employees donated a total of $4,500 to the political action committee (PAC) Partners for Progress in early June. Partners for Progress reported it gave more than $10,000 in both direct and in-kind contributions to Mayor Tony Mack in the two weeks leading up to his victory in the mayoral runoff election June 15."
## [5] "And when it's often difficult to predict a law's impact, legislators should think twice before carrying any bill. Is it absolutely necessary? Is it an issue serious enough to merit their attention? Will it definitely not make the situation worse?"
Sampling
Sample only 10% of the data since the dataset is so large
set.seed(48)
news.s <- sample(news, length(news)*0.10, replace = FALSE)
twitter.s <- sample(twitter, length(twitter)*0.10, replace = FALSE)
blogs.s <- sample(blogs, length(blogs)*0.10, replace = FALSE)
hist(unlist((lapply(news.s,stri_length))),breaks=100,main = "Number of Words per Line (News)", xlab = "Number of Words", ylab = "Frequency")
hist(unlist((lapply(twitter.s,stri_length))),breaks=100,main = "Number of Words per Line (Twitter)", xlab = "Number of Words", ylab = "Frequency")
## Warning: closing unused connection 7 (final/en_US/en_US.news.txt)
## Warning: closing unused connection 6 (final/en_US/en_US.blogs.txt)
## Warning: closing unused connection 5 (final/en_US/en_US.twitter.txt)
hist(unlist((lapply(blogs.s,stri_length))),breaks=100,main = "Number of Words per Line", xlab = "Number of Words per Line (Blogs)", ylab = "Frequency")
Data Cleaning
Combine the data files into one file - then remove numbers, special characters (like those pesky hashtags), and extra white space
library(tm)
library(SnowballC)
tbn <- c(news.s,twitter.s,blogs.s)
corpus <- Corpus(VectorSource(tbn))
remove.decimals <- function(x) {gsub("([0-9]*)\\.([0-9]+)", "\\1 \\2", x)}
remove.hashtags <- function(x) { gsub("#[a-zA-z0-9]+", " ", x)}
remove.nonenglish <- function(x) {gsub("\\W+", " ",x)}
corpus <- tm_map(corpus, remove.decimals)
corpus <- tm_map(corpus, removeNumbers)
corpus <- tm_map(corpus, remove.nonenglish)
corpus <- tm_map(corpus, remove.hashtags)
corpus <- tm_map(corpus, stripWhitespace)
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, tolower)
corpus <- tm_map(corpus, removeWords, stopwords("english"))
Remove Profane and Offensive Words
badwords <- read.csv("en.csv", header = F)
badwords <- rep(badwords$V1)
corpus <- tm_map(corpus, removeWords,badwords)
Task 2: Exploratory Data Analysis
Tokenization
One-Gram Tokenization
options( java.parameters = "-Xmx4g" )
library(rJava)
library(RWeka)
##
## Attaching package: 'RWeka'
##
## The following objects are masked from 'package:foreign':
##
## read.arff, write.arff
options(mc.cores = 1)
one.gram_Tokenizer <- NGramTokenizer(corpus, Weka_control(min = 1, max = 1))
one.gram <- data.frame(table(one.gram_Tokenizer))
one.gram.sort <- one.gram[order(one.gram$Freq,decreasing = TRUE),]
one.gram.20 <- one.gram.sort[1:20,]
colnames(one.gram.20) <-c("Word","Frequency")
Top 20 Single Words
ggplot(one.gram.20, aes(x=Word,y=Frequency), ) + geom_bar(stat="Identity", fill="blue") +geom_text(aes(label=Frequency), vjust=-0.2)
## Error: could not find function "ggplot"
Two-Gram Tokenization
two.gram_Tokenizer <- NGramTokenizer(tbn, Weka_control(min = 2, max = 2))
two.gram <- data.frame(table(two.gram_Tokenizer))
two.gram.sort <- two.gram[order(two.gram$Freq,decreasing = TRUE),]
two.gram.20 <- two.gram.sort[1:20,]
colnames(two.gram.20) <-c("Word","Frequency")
Top 20 Double Words
ggplot(two.gram.20, aes(x=Word,y=Frequency), ) + geom_bar(stat="Identity", fill="light blue") +geom_text(aes(label=Frequency), vjust=-0.2)
## Error: could not find function "ggplot"
Three-Gram Tokenization
three.gram_Tokenizer <- NGramTokenizer(tbn, Weka_control(min = 3, max = 3))
three.gram <- data.frame(table(three.gram_Tokenizer))
three.gram.sort <- three.gram[order(three.gram$Freq,decreasing = TRUE),]
three.gram.20 <- three.gram.sort[1:20,]
colnames(three.gram.20) <-c("Word","Frequency")
Top 20 Triple Words
ggplot(three.gram.20, aes(x=Word,y=Frequency), ) + geom_bar(stat="Identity", fill="green") +geom_text(aes(label=Frequency), vjust=-0.2)
## Error: could not find function "ggplot"
Word Cloud for Single and Double Words
par(mfrow = c(1,2))
library(wordcloud)
## Loading required package: Rcpp
## Loading required package: RColorBrewer
## Loading required package: RColorBrewer
wordcloud(one.gram.sort[,1],freq=one.gram.sort[,2],scale=c(5,1),random.order=F,rot.per=0.5,min.freq=100,colors=brewer.pal(8,"Dark2"))
wordcloud(two.gram.sort[,1],freq=one.gram.sort[,2],scale=c(5,1),random.order=F,rot.per=0.5,min.freq=100,colors=brewer.pal(8,"Dark2"))
Future Directions
Address apostrophe issues
Build a predictive Model.
Build a Shiny Model.