Milestone Report
Synopsis
The report provides a short overview of the exploratory analysis of the text data for the Capstone project.
The motivation for this project is to: 1. Demonstrate that you’ve downloaded the data and have successfully loaded it in. 2. Create a basic report of summary statistics about the data sets. 3. Report any interesting findings that you amassed so far. 4. Get feedback on your plans for creating a prediction algorithm and Shiny app.
Data Loading and Analysis
path = 'https://d396qusza40orc.cloudfront.net/dsscapstone/dataset/Coursera-SwiftKey.zip'
download.file(path,method = 'curl',destfile = 'swiftkey.zip')
#unzip
unzip('swiftkey.zip')Basic Summary
The data consists of 3 sources: blogs, twitter, news.
library(stringr)
twitter= suppressWarnings(readLines('final/en_US/en_US.twitter.txt'))
news = suppressWarnings(readLines('final/en_US/en_US.news.txt'))
blog = suppressWarnings(readLines('final/en_US/en_US.blogs.txt'))
ltwitter = suppressWarnings(length(twitter))
lnews = suppressWarnings(length(news))
lblog = suppressWarnings(length(blog))
wtwitter = sum(str_count(twitter))
wnews = sum(str_count(news))
wblog = sum(str_count(blog))
summary = data.frame(file.name=c("en_US.blogs.txt","en_US.twitter.txt","en_US.news.txt"),
lines.count=c(lblog,ltwitter,lnews),
word.count=c(wblog,wtwitter,wnews))
summary## file.name lines.count word.count
## 1 en_US.blogs.txt 899288 206824257
## 2 en_US.twitter.txt 2360148 162095755
## 3 en_US.news.txt 1010242 203223153Fraction of Data
I will use random sample of 1% for further analysis since the file is rather large.
set.seed(100)
blogs_sample = sample(blog, 0.01*length(blog), replace = FALSE)
news_sample = sample(news, 0.01*length(news), replace = FALSE)
twitter_sample = sample(twitter, 0.01*length(twitter), replace = FALSE)
sampled_data = c(blogs_sample, news_sample, twitter_sample)Build corpus
We need to clean the data first. We will remove numbers, punctations, white space, and stopwords.
library(wordcloud)## Loading required package: RColorBrewerlibrary(RColorBrewer)
library(NLP)
library(SnowballC)
library(tm)
sample_data = iconv(sampled_data, 'UTF-8', 'ASCII')
corpus = Corpus(VectorSource(as.data.frame(sample_data, stringsAsFactors = FALSE)))
corpus = corpus %>%
tm_map(tolower) %>%
tm_map(removePunctuation)%>%
tm_map(removeNumbers)%>%
tm_map(stripWhitespace)%>%
tm_map(removeWords, stopwords("en"))%>%
tm_map(PlainTextDocument)## Warning in as.POSIXlt.POSIXct(Sys.time(), tz = "GMT"): unknown timezone
## 'zone/tz/2018c.1.0/zoneinfo/America/Los_Angeles'Plots
You can also embed plots, for example:
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N-gram
library(ggplot2)##
## Attaching package: 'ggplot2'## The following object is masked from 'package:NLP':
##
## annotatelibrary(RWeka)
trigram <- NGramTokenizer(corpus, Weka_control(min = 3, max = 3))
trigram.df <- data.frame(table(trigram))
trigram.df <- trigram.df[order(trigram.df$Freq, decreasing = TRUE),]
ggplot(trigram.df[1:25,], aes(x=trigram, y=Freq)) +
geom_bar(stat="Identity", fill="blue")+
xlab("Trigrams") + ylab("Frequency")+
ggtitle("Most common 25 Trigrams") +
theme(axis.text.x=element_text(angle=90, hjust=1))
Summary
Based on the exploratory analysis performed, the strategy would be to utilize a frequency lookup using ngram models. However, further cleansing of data and reduction of data is needed since n-grams takes a long time even when down sampling to 1%. Caching should help speeding the process up.
Recap: 1. Import Data 2. Cleanse Data 3. Using n-grams model 4. Plot the n-gram model
To improve: 1. Better sample size 2. More data cleansing 3. Determine test and train dataset for later models.