R: Text Mining (Pre-processing)

This is part 2 of my Text Mining Lesson series. If you haven’t already, please check out part 1 that covers Term Document Matrix: R: Text Mining (Term Document Matrix)

Okay, now I promise to get to the fun stuff soon enough here, but I feel that in most tutorials I have seen online, the pre-processing of text is often glanced over. It was always (and often still is) a real sore spot for me when assumptions are made as to my knowledge level. If you are going to throw up a block of code, at least give a line or two explanation as to what the code is there for, don’t just assume I know.

I remember working my way through many tutorials where I was able to complete the task by simply copying the code, but I didn’t have a full grasp of what was happening in the middle. I this lesson, I am going to cover some of the more common text pre-processing steps used in the TM library. I am going to go into some level of detail and make some purposeful mistakes so hopefully when you are done here you will have a firm grasp on this very important step in the text mining process.

Let’s start by getting our libraries

install.packages("tm") # if not already installed


Now, let’s load our data. For this lesson we are going to use a simple vector.

wordVC <- c("I like dogs!", "A cat chased the dog.", "The dog ate a bone.", 
            "Cats make fun pets.", "The cat did what? Don't tell the dog.", 
            "A cat runs when it has too, but most dogs love running!")

Now let’s put this data into a corpus for text processing.

corpus <- (VectorSource(wordVC))
corpus <- Corpus(corpus)

Here is the output


Now for a little frustration. Let’s say you want to see what is in text document 4. You could try


But this will be your output, not really what you are looking for.


If you want to see the actual text- try this instead


Now you can see the text


As we go through the text pre-processing, we are going to use the following For Loop to examine our corpus

for (i in 1:6) print (corpus[[i]]$content)




Punctuation generally adds no value to text mining when utilizing standard numeric based data mining algorithms like clustering or classification. So it behooves us to just remove it.

To do so, the tm package has a cool function called tm_map() that we can pass arguments to, such as removePunctuation

corpus <- tm_map(corpus, content_transformer(removePunctuation))

for (i in 1:6) print (corpus[[i]]$content)

Note, you do not need the for loop, I am simply running it each time to show you the progress. 

Notice all the punctuation is gone now.



Next we are going to get rid of what are known as stopwords. Stopwords are common words such as (the, an, and, him, her). These words are so commonly used that they provide little insight as to the actual meaning of the given text.  To get rid of them, we use the following code.

corpus <- tm_map(corpus, content_transformer(removeWords), 
for (i in 1:6) print (corpus[[i]]$content)

If you look at line 2, “A cat chase  dog”, you will the word “the” has been removed. However, if you look at the next line down, you will notice “The” is still there.



Well it comes down to the fact that computers do not treat T and t as the same letter, even though they are. Capitalized letters are viewed by computers as separate entities. So “The” doesn’t match “the” found in the list of stopwords to remove.

For a full list of R stopwords, go to: https://github.com/arc12/Text-Mining-Weak-Signals/wiki/Standard-set-of-english-stopwords

So how do we fix this?


Using tm_map with the “tolower” argument will make all the letters lowercase. If we then re-run our stopwords command, you will see all the “the” are gone

corpus <- tm_map(corpus, content_transformer(tolower))
corpus <- tm_map(corpus, content_transformer(removeWords), 
for (i in 1:6) print (corpus[[i]]$content)




Next we will stem our words. I covered this in the last lesson, but it bears repeating. What stemming does is attempt to remove variants of words. In our example, pay attention to the following words (dog, dogs, cat, cats, runs, running)

corpus <- tm_map(corpus, stemDocument)
for (i in 1:6) print (corpus[[i]]$content)

Notice the words are now (dog, cat, run)



Finally, let’s get rid of all this extra white space we have now.

corpus <- tm_map(corpus, stripWhitespace) 
for (i in 1:6) print (corpus[[i]]$content)




I didn’t use this argument with my tm_map() function today because I did not have any numbers in my text. But if I, the command would be as follows

corpus <- tm_map(corpus, content_transformer(removeNumbers))



R: Text Mining (Term Document Matrix)

There are a bounty of well known machine learning algorithms, both supervised (Decision Tree, K Nearest Neighbor, Logistical Regression) and unsupervised (clustering, anomaly detection). The only catch is that these algorithms are designed to work with numbers, not text. The act of using numeric based data mining methods on text is known as duo-mining.

So before you can utilize these algorithms, you first have to  transform text into a format suitable for use in these number based algorithms. One of the most popular methods people first learn is how to create a Term Document Matrix (TDM). The easiest way to understand this concept is, of course, to do an example.

Let’s start by loading the required library

install.packages("tm") # if not already installed

Now let’s create a simple vector of strings.

wordVC <- c("I like dogs", "A cat chased the dog", "The dog ate a bone", 
            "Cats make fun pets")

Now we are going to place the strings into a data type designed for text mining (from the tm package) called corpus. A corpus simply means the full collection of text you want to work with.

corpus <- (VectorSource(wordVC))
corpus <- Corpus(corpus)


 Length Class Mode
1 2 PlainTextDocument list
2 2 PlainTextDocument list
3 2 PlainTextDocument list
4 2 PlainTextDocument list

As you can see from the summary, the corpus classified each string in the vector as a PlaintextDocument.

Now let’s create our first Term Document Matrix

TDM <- TermDocumentMatrix(corpus)



As you can see, we now have a numeric representation of our text. Each row represents a word in our text and each column represents an individual sentence. So if you look at the word dog, you can see it appears once in sentence 2 and 3, (0110). While bone appears once in sentence 3 only (0010).


One immediate issue that jumps out at me is that R now sees the words cat & cats and dog & dogs as different words, when really cats and dogs are just the plural versions of cat and dog. Now there may be some more advanced applications of text mining that you would want to keep the two words separate, but in most basic text mining applications, you would want to only keep one version of the word.

Luckily for us, R makes that simple. Use the function tm_map with the argument stemDocument

corpus2 <- tm_map(corpus, stemDocument)

make a new TDM

TDM2 <- TermDocumentMatrix(corpus2) 

Now you see only the singular of cat and dog exist in our list


If you would like, you can also work with the transpose of the TDM called the Document Term Matrix.

dtm = t(TDM2)




I’ll get deeper into more pre-processing tasks, as well as ways to work with your TDM in future lessons. But for now, practice making TDMs see if you can think of ways that you can use TDMs and DTMs with some machine learning algorithms you might already know (decision trees, logistic regression).

R: Creating a Word Cloud

Word Clouds are great visualization techniques for dealing with text analytics. The idea behind them is they display the most common words in a corpus of text. The more often a word is used, the larger and darker it is.


Making a word cloud in R is relatively easy. The tm and wordcloud libraries from R’s CRAN repository is used to create one.


If you do not have either of these loaded on your machine, you will have to use the following commands


Now in order to make a word cloud, you first need a collection of words. In our example I am going to use a text file I created from the Wikipedia page on R.

You can download the text file here: rwiki

Now let’s load the data file.

text <- readLines("rWiki.txt")
> head(text)
[1] "R is a programming language and software environment 
[2] "The R language is widely used among statisticians and 
[3] "Polls, surveys of data miners, and studies of scholarly 
[4] "R is a GNU package.[9] The source code for the R 
[5] "General Public License, and pre-compiled binary versions
[6] "R is an implementation of the S programming language "

Notice each line in the text file is an individual element in the vector –  text

Now we need to move the text into a tm element called a Corpus. First we need to convert the vector text into a VectorSource.

wc <- VectorSource(text)
wc <- Corpus(wc)

Now we need to pre-process the data. Let’s start by removing punctuation from the corpus.

wc <- tm_map(wc, removePunctuation)

Next we need to set all the letters to lower case. This is because R differentiates upper and lower case letters. So “Program” and “program” would treated as 2 different words. To change that, we set everything to lowercase.

wc <- tm_map(wc, content_transformer(tolower))

Next we will remove stopwords. Stopwords are commonly used words that provide no value to the evaluation of the text. Examples of stopwords are: the, a, an, and, if, or, not, with ….

wc <- tm_map(wc, removeWords, stopwords("english"))

Finally, let’s strip away the whitespace

wc <- tm_map(wc, stripWhitespace)

Now let us make our first word cloud

The syntax is as follows – wordcloud( words = corpus, scale = physical size, max.word = number of words in cloud)

wordcloud(words = wc, scale=c(4,0.5), max.words=50)


Now we have a word cloud, let’s add some more elements to it.

random.order = False brings the most popular words to the center

wordcloud(words = wc, scale=c(4,0.5), max.words=50,random.order=FALSE)


To add a little more rotation to your word cloud use rot.per

wordcloud(words = wc, scale=c(4,0.5), max.words=50,random.order=FALSE,

Finally, lets add some color. We are going to use brewer.pal.  The syntax is brewer.pal(number of colors, color mix)

cp <- brewer.pal(7,"YlOrRd")
wordcloud(words = wc, scale=c(4,0.5), max.words=50,random.order=FALSE,
 rot.per=0.25, colors=cp)





R: K-Means Clustering- Deciding how many clusters

In a previous lesson I showed you how to do a K-means cluster in R. You can visit that lesson here: R: K-Means Clustering.

Now in that lesson I choose 3 clusters. I did that because I was the one who made up the data, so I knew 3 clusters would work well. In the real world it doesn’t work that way. Choosing the right number of clusters is one of the trickier parts of performing a k-means cluster.

If you go over to Michael Grogan’s site, you will see he has a great method for figuring out how many clusters to choose. http://www.michaeljgrogan.com/k-means-clustering-example-stock-returns-dividends/

wss <- (nrow(sample_stocks)-1)*sum(apply(sample_stocks,2,var))
for (i in 2:20) wss[i] <- sum(kmeans(sample_stocks,
plot(1:20, wss, type="b", xlab="Number of Clusters",
ylab="Within groups sum of squares")

If you understand the code above, then great. That is a great solution for choosing the number of clusters. If, however, you are not 100% sure what is going on above, keep reading. I’ll walk you through it.

K-Means Clustering

We need to start by getting a better understanding of what k-means clustering means. Consider this simplified explanation of clustering.

The way is works is each of the rows our data are placed into a vector.


These vectors are then plotted out in space. Centroids (the yellow stars in the picture below) are chosen at random. The plotted vectors are then placed into clusters based on which centroid they are closest to.


So how do you measure how good your clusters fit. (Do you need more clusters? Less clusters)? One popular metrics is the Within cluster sum of squares. R provides this as kmeans$withinss. What this means is the distance the vectors in each cluster are from their respected centroid.

The goal is to get the is to get this number as small as possible. One approach to handling this is to run your kmeans clustering multiple times, raising the number of the clusters each time. Then you compare the withinss each time, stopping when the rate of improvement drops off. The goal is to find a low withinss while still keeping the number of clusters low.


This is, in effect, what Michael Grogan has done above.

Break down the code

Okay, now lets break down Mr. Grogan’s code and see what he is doing.

wss <- (nrow(sample_stocks)-1)*sum(apply(sample_stocks,2,var))
for (i in 2:20) wss[i] <- sum(kmeans(sample_stocks,
plot(1:20, wss, type="b", xlab="Number of Clusters",
ylab="Within groups sum of squares")


The first line of code is a little tricky. Let’s break it down.

wss <- (nrow(sample_stocks)-1)*sum(apply(sample_stocks,2,var))

sample_stocks – the data set

wss <-  – This simply assigns a value to a variable called wss

(nrow(sample_stocks)-1)  – the number of rows (nrow) in sample_stocks – 1. So if there are 100 rows in the data set, then this will return 99

sum(apply(sample_stocks,2,var)) – let’s break this down deeper and focus on the apply() function. apply() is kind of like a list comprehension in Python. Here is how the syntax works.

apply(data, (1=rows, 2=columns), function you are passing the data through)

So, let’s create a small array and play with this function. It makes more sense when you see it in action.

 tt <- array(1:20, dim=c(10,2)) # create array with data 1 -20, 
                                #10 rows, 2 columns
> tt
 [,1] [,2]
 [1,] 1 11
 [2,] 2 12
 [3,] 3 13
 [4,] 4 14
 [5,] 5 15
 [6,] 6 16
 [7,] 7 17
 [8,] 8 18
 [9,] 9 19
[10,] 10 20

Now lets try running this through apply.

> apply(tt, 2, mean)
[1] 5.5 15.5

Apply took the mean of each column. Had I used 1 as the second argument, it would have taken the mean of each row.

> apply(tt, 1, mean)
 [1] 6 7 8 9 10 11 12 13 14 15

Also, keep in mine, you can create your own functions to be used in apply

apply(tt,2, function(x) x+5)
 [,1] [,2]
 [1,] 6 16
 [2,] 7 17
 [3,] 8 18
 [4,] 9 19
 [5,] 10 20
 [6,] 11 21
 [7,] 12 22
 [8,] 13 23
 [9,] 14 24
[10,] 15 25

So, what is Mr. Grogan’s doing with his apply function? apply(sample_stocks,2,var) – He is taking the variance of each column his data set.

[1] 9.166667 9.166667

And by summing it: sum(apply(sample_stocks,2,var)) – he is simply adding the two values together.

[1] 18.33333

So, the entire first line of code using our data is:

wss <- (nrow(tt)-1)*sum(apply(tt,2,var))

wss <- (10-1) * (18.333)

wss <- (nrow(tt)-1)*sum(apply(tt,2,var))
> wss
[1] 165

What this number is effectively is the within sum of squares for a data set that has only one cluster

Next section of code

Next we will tackle the next two lines of code.

for (i in 2:20) wss[i] <- sum(kmeans(sample_stocks,

The first part is a for loop and should be simple enough. Note he doesn’t use {} to denote the inside of his loop. You can do this when your for loop is a single line, but I am going to use the {}’s anyway, as I think it makes the code a bit neater.

for (i in 2:20)  — a for loop iterating from 2 -20

for (i in 2:20) {

wss[i] <- }  – we are going to assign more values to the vector wss starting at 2 and working our way down to 20.

Remember, a single value variable in R is actually a single value vector.

c <- 5
> c
[1] 5
> c[2] <- 7
> c
[1] 5 7

Okay, so now to the trickier code. sum(kmeans(sample_stocks, centers = i)$withinss)

What he is doing is running a kmeans cluster for the data one time each for each value of centers (number of centroids we want) from 2 to 20 and reading the $withinss from each run. Finally it sums all the withinss up (you will have 1 withinss for every cluster you create – number of centers)

Plot the results

The last part of the code is plotting the results

plot(1:20, wss, type="b", xlab="Number of Clusters",
ylab="Within groups sum of squares")

plot (x, y, type= type of graph, xlab = label for x axis, ylab= label for y axis

Let’s try it with our data

If you already did my Kmeans lesson, you should already have the file, if not you can download it hear. cluster

 myData <- read.csv('cluster.csv')
> head(myData)
 StudentId TestA TestB
1 2355645.1 134 24
2 8718152.6 155 32
3 8303333.6 130 25
4 6352972.5 185 86
5 3381543.2 153 95
6 817332.4 153 81
> myData <- myData [,2:3] # get rid of StudentId column
> head(myData)
 TestA TestB
1 134 24
2 155 32
3 130 25
4 185 86
5 153 95
6 153 81

Now lets feed this through Mr. Grogan’s code

wss <- (nrow(myData)-1)*sum(apply(myData,2,var))
for (i in 2:20) {
          wss[i] <- sum(kmeans(myData,
plot(1:20, wss, type="b", xlab="Number of Clusters",
ylab="Within groups sum of squares")

Here is our output ( a scree plot for you math junkies out there)


Now Mr. Grogan’s plot a nice dramatic drop off, which is unfortunately not how most real world data I have seen works. I am going to chose 5 as my cut off point, because while the withinss does continue to decrease, it doesn’t seem to do so at a rate great enough to accept the added complexity of more clusters.

If you want to see how 5 clusters looks next to the three I had originally created, you can run the following code.

myCluster <- kmeans(myData,5, nstart = 20)
myData$cluster <- as.factor(myCluster$cluster)
ggplot(myData, aes(TestA, TestB, color = cluster))
+ geom_point()

5 Clusters


3 Clusters


I see some improvement in the 5 cluster model. So Michael Grogan’s trick for finding the number of clusters works.


R: K-Means Clustering

Note: This is an introductory lesson with a made up data set. After you are finished with this tutorial, if you want to see a nice real world example, head on over to Michael Grogan’s website:


K Means Cluster will be our introduction to Unsupervised Machine Learning. What is Unsupervised Machine Learning exactly? Well, the simplest explanation I can offer is that unlike supervised where our data set contains a result, unsupervised does not.

Think of a simple regression where I have the square footage and selling prices (result) of 100 houses. Taking that data, I can easily create a prediction model that will predict the selling price of a house based off of square footage. – This is supervised machine learning

Now, take a data set containing 100 houses with the following data: square footage, house style, garage/no garage, but no selling price. We can’t create a prediction model since we have no knowledge of prices, but we can group the houses together based on commonalities. These groupings (clusters) can be used to gain knowledge of your data set.

I think seeing it in action will help.

Here is the data set: cluster

The data we will be looking at test results for 149 students.


The task at hand is to group the students into 3 groups based on the test results. Now one thing any teacher will let you know is that some kids perform well in one subject and perhaps not so well in another. So we can’t simply group them on the score performance on one test. And when you are dealing with real world data, you might be looking at 20 -100 test/quiz scores per student.

So what we are going to do is let the computer decide how to group (or cluster) them.

To do so, we are going to be using K-means clustering. K-means clustering works by choosing random points (centroids). It then groups the data points around the centroids based which centroid the points are closest to.

Let’s get started

Let’s start by loading the data

st <- read.csv(file.choose())

our data


Now let’s run the data through a Kmeans() algorithm

First, we are only going to want to focus on columns 2 and 3 in the data set since column 1 (studentID) is basically a label and provides no value in prediction.

To do this, we subset the data: st[,2:3] – which means I want all row ([,) and columns 2-3 (2:3])


Now the code to make our clusters

stCl <- kmeans(st[, 2:3], 3, nstart = 20)

The syntax is kmeans(DATA, Number of clusters, Numbers of random starts)

Number of clusters I picked as 3 because I know this works well with the data, picking the right number usually takes a little trial and error in real life

Number of random starts is how many times you want the algorithm to be rerun (choosing new centroids each time) and choosing the result where the clusters are tightest.

Below is the output of our Kmeans – note the cluster means, this tells us the mean score for TestA and TestB set in each cluster.


Hey, if you are a math junkie, this may be all you want. But if you are looking for some more practical value here, lets move on.

First, we need to add a column to our data set that shows our columns.

Now since we read our data from a csv, it is a data frame. If you can’t remember that, you can always run the command is.data.frame(st) to test it out.

Do you remember how to add a column to a data frame?

Well, there are multiple ways, but this is, in my opinion, the easiest way.

st$cluster <- stCl$cluster

st$cluster <- stCl$cluster

Here is the result


Now with the clusters, you can group your students based their assigned cluster.

Technically we are done here. We have successfully grouped the students. But what if you want to make sure you did a good job. One quick check is to graph your work.

Before we can graph, we have to make sure our st$cluster column is set as a factor, then using ggplot, we can graph it. (if you don’t have ggplot2 installed, you will need to run this line: install.packages(“ggplot2”)

st$cluster <- as.factor(st$cluster)
ggplot(st, aes(TestA, TestB, color = cluster)) + geom_point()

And here is our output. The groups look pretty good.


R: Working with lists

Lists in R allow you to store data of different types into a single data structure. While they are extremely useful, they can be a bit confusing to work with at first.

Let’s start by creating a list. The syntax is simple enough, just add list() around the elements you want to put in your list.

l1 <- list(24, c(12,15,19), "Dogs")

Here is the output. Note we have 3 different groupings. 1 number, 1 vector (with 3 elements) and 1 character


You can call on each element using the element names (found in the double brackets [[]])


Here are the results


Now, what if you want to call 1 element from the vector in [[2]]

You do this by adding [] to the end of the line


This will give you the 3rd number in the vector found in [[2]]


To make it easier to work with lists, you can rename the elements.

names(l1)  # shows NULL since the elements have no names
names(l1) = c("Number", "Vector", "Char")
names(l1) # now shows assigned names


Now you can call on the list using the names.

l1$Char # will return "Dogs"

l1$Vector[2] #will return the second number in the vector in the list

You can also simply name your elements when creating your list

rm(l1) #deletes list
l1 <- list(Number=24,Vector = c(12,15,19), Char="Dogs")

You can add a new element to you list via number

l1[[4]] = "New Element"

Even better, you can add via new name

l1$Char2 <- "Cat"

Now let’s look at our list


Now we can use the names() function to give element 4 a name, or we can just get rid of it.

To delete an elements, use NULL

l1[[4]] <- NULL

Now the last thing we will cover is how to subset a list

l1[1:3] # gives us elements 1 -3


We want to pick some elements out of order




R: Converting Factors to Numbers

R, like all programming languages, has its quirks. One of the more frustrating ones is the way it acts when trying to convert a factor into a numeric variable.

Let’s start with a vector of numbers that have been mistakenly loaded as characters.

chars <- c("12","13","14","12","11","13","12")

Here is the output


Now, let’s convert this vector to a numeric vector using the function as.numeric()

nums <- as.numeric(chars)

And here is the output


As you can see it works fine.

But now let’s try it with a factor

fac <- factor(c("12","13","14","12","11","13","12"))

Here is the output.


Now look what happens when I try the as.numeric() function

nums <- as.numeric(fac)

Check out the results


While is says the type is a double, clearly the numbers are not correct.

How to fix it?

Well, the secret is that first you need to convert the factor into a character, then into a numeric.

nums <- as.numeric(as.character(fac))

Now check out the results


Now we have the correct numbers. Just keep this little trick in mind. It has caused me some undue frustration in past.


R: Twitter Sentiment Analysis

Having a solid understanding of current public sentiment can be a great tool. When deciding if a new marketing campaign is being met warmly, or if a news release about the CEO is causing customers get angry, people in charge of handling a company’s public image need these answers fast. And in the world of social media, we can get those answers fast. One simple, yet effective, tool for testing the public waters is to run a sentiment analysis.

A sentiment analysis works like this. We take a bunch of tweets about whatever we are looking for (in this example we will be looking at President Obama). We then parse those tweets out into individual words and we count the number of positive words and compare it to the number of negative words.

Now the simplicity of this model misses out on some things. Sarcasm can easily missed. Ex. “Oh GREAT job Obama. Thanks for tanking the country once again”. Our model will count 2 positive words (Great and Thanks) and 1 negative word (tanking) giving us an overall score of positive 1.

There are more complex methods for dealing with the issue above, but you’ll be surprised at how good the system works all by itself. While, yes we are going to misread a few tweets, we have the ability to read thousands of tweets, so the larger volume of data negates the overall effect of the sarcastic ones.

First thing we need to do is go get a list of good and bad words. You could make your own up, but there are plenty of pre-populated lists on the Internet for free. The one I will be using is from the University of Illinois at Chicago. You can find the list here:


Once you go to the page, click on Opinion Lexicon and then download the rar file.

You can dowload from the link below, but I want you to know the source in case this link breaks.

Now open the rar file and move the two text files to a folder you can work from.

Next let’s make sure we have the right packages installed. For this we will need, TwitteR, plyr, stringr, and xlsx. If you do not  have these packages installed, you can do so using the following code. (just change out TwitteR for whatever package you need to install)


Now load the libraries


and connect to the Twitter API. If you do not already have a connection set up, check out my lesson on connecting to Twitter: R: Connect to Twitter with R

api_key<- "insert consumer key here"
api_secret <- "insert consumer secret here"
access_token <- "insert access token here"
access_token_secret <- "insert access token secret here

Okay, so now remember where you stored the text files we just downloaded and set that location as your working directory (wd). Note that we use forward slashes here, even if you are on a Windows box.

neg = scan("negative-words.txt", what="character", comment.char=";")
pos = scan("positive-words.txt", what="character", comment.char=";")

scan looks through the text files and pulls words that start with characters and ignores comment lines that start with ;

You should now have 2 lists of positive and negative words.

You can add words to either list using a  vector operation. Below I added wtf – a popular Internet abbreviation for What the F@#$@ to the negative words

neg = c(neg, 'wtf')

Okay, now here is the engine that runs our analysis. I have tried to comment on what certain commands you may not recognize do.  I have lessons on most features listed here, and will make more lessons on anything missing. If I were to try to explain this step by step, this page would be 10000 lines long and no one would read it.

score.sentiment = function(tweets, pos.words, neg.words)

scores = laply(tweets, function(tweet, pos.words, neg.words) {

tweet = gsub('https://','',tweet) # removes https://
tweet = gsub('http://','',tweet) # removes http://
tweet=gsub('[^[:graph:]]', ' ',tweet) ## removes graphic characters 
       #like emoticons 
tweet = gsub('[[:punct:]]', '', tweet) # removes punctuation 
tweet = gsub('[[:cntrl:]]', '', tweet) # removes control characters
tweet = gsub('\\d+', '', tweet) # removes numbers
tweet=str_replace_all(tweet,"[^[:graph:]]", " ") 

tweet = tolower(tweet) # makes all letters lowercase

word.list = str_split(tweet, '\\s+') # splits the tweets by word in a list
words = unlist(word.list) # turns the list into vector
pos.matches = match(words, pos.words) ## returns matching 
          #values for words from list 
neg.matches = match(words, neg.words)
pos.matches = !is.na(pos.matches) ## converts matching values to true of false
neg.matches = !is.na(neg.matches)
score = sum(pos.matches) - sum(neg.matches) # true and false are 
                #treated as 1 and 0 so they can be added
}, pos.words, neg.words )
scores.df = data.frame(score=scores, text=tweets)

Now let’s get some tweets and analyze them. Note, if your computer is slow or old, you can lower the number of tweets to process. Just change n= to a lower number like 100 or 50

tweets = searchTwitter('Obama',n=2500)
Tweets.text = laply(tweets,function(t)t$getText()) # gets text from Tweets

analysis = score.sentiment(Tweets.text, pos, neg) # calls sentiment function

Now lets look at the results. The quickest method available to us is to simply run a histogram


My results looks like this


If 0 is completely neutral most people are generally neutral about the president and more people have positives tweets then negatives ones. This is not uncommon for an outgoing president. They generally seem to get a popularity boost after the election is over.

Finally, if you want to save your results, you can export them to excel.

write.xlsx(analysis, "myResults.xlsx")

And you will end up with a file like this


R: Decision Trees (Regression)

Decision Trees are popular supervised machine learning algorithms. You will often find the abbreviation CART when reading up on decision trees. CART stands for Classification and Regression Trees.

In this example we are going to create a Regression Tree. Meaning we are going to attempt to build a model that can predict a numeric value.

We are going to start by taking a look at the data. In this example we are going to be using the Iris data set native to R. This data set



As you can see, our data has 5 variables – Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species. The first 4 variables refer to measurements of flower parts and the species identifies which species of iris this flower represents.

In the Classification example, we tried to predict the Species of flower. In this example we are going to try to predict the Sepal.Length

In order to build our decision tree, first we need to install the correct package.



Next we are going to create our tree. Since we want to predict Sepal.Length – that will be the first element in our fit equation.

fit <- rpart(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width+ Species, 
 method="anova", data=iris )

Note the method in this model is anova. This means we are going to try to predict a number value. If we were doing a classifier model, the method would be class.

Now let’s plot out our model

plot(fit, uniform=TRUE, 
 main="Regression Tree for Sepal Length")
 text(fit, use.n=TRUE, cex = .6)

Note the splits are marked – like the top split is Petal.Length < 4.25

Also, at the terminating point of each branch, you see and n= . The number following this is the number of elements from the data file that fit at the end of that branch.


While this model actually works out pretty good, one thing to look for is over fitting. A good sign of that would be having a bunch of branches terminating with n values of 1 or 2. This means the model is tuned too much to the test data and when run up against a new set of data it will most likely result in poor predictions.

Of course we can look at some of the numbers if you are so inclined.


Notice the xerror (cross validation error) gets better with each split. That is something you want to look out for. If that number starts to creep up as the splits increase, that is a sign you may want to prune some of the branches. I will show how to do that in another lesson.

To get a better picture of the change in xerror as the splits increase, let’s look at a new visualization


This produces 2 charts, 1rst on shows how R-Squared improves as splits increase (remember R-squared gets better as it approaches 1 so this model is improving with each spit)

The second chart shows how xerror decreases with each split. For models that need pruning, you would see the curve starting to go back up as the splits increase. Imagine is split 6 was higher than split 5.


Okay, so finally now that we know the model is good, let’s make a prediction.

testData  <-data.frame (Species = 'setosa', Sepal.Width = 4, Petal.Length =1.2,
predict(fit, testData, method = "anova")


So as you can see, based on our test data, the model predicts our Sepal.Length will be approx 5.17.


R: Decision Trees (Classification)

Decision Trees are popular supervised machine learning algorithms. You will often find the abbreviation CART when reading up on decision trees. CART stands for Classification and Regression Trees.

In this example we are going to create a Classification Tree. Meaning we are going to attempt to classify our data into one of the (three in this case) classes.

We are going to start by taking a look at the data. In this example we are going to be using the Iris data set native to R. This data set



As you can see, our data has 5 variables – Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species. The first 4 variables refer to measurements of flower parts and the species identifies which species of iris this flower represents. What we are going to attempt to do here is develop a predictive model that will allow us to identify the species of iris based on measurements.

The species we are trying to predict are setosa, virginica, and versicolor. These are our three classes we are trying to classify our data as.

In order to build our decision tree, first we need to install the correct package.



Next we are going to create our tree. Since we want to predict Species – that will be the first element in our fit equation.

fit <- rpart(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
 method="class", iris)

Now, let’s take a look at the tree.


To understand what the output says, according to our model, if the Pedal.Length is < 2.45 then the flower is classified as setosa. If not, it goes to the next split – Petal Width. If < 1.75 then versicolor, else virginica.


Now, we want to take a look at how good the model is.


I am not going to harp too much on the stats here, but lets look down at the table on the bottom. The first row has a CP = 0.50.  This means (approx) that the first split reduced the relative error by 0.5. You can see this in the rel error in the second row.

Now the 2nd row CP = 0.44, so the second split improved the rel error in the third row to 0.06.

Now personally, when just trying to get a quick overview of the goodness of the model, I look at the xerror (cross validation error) of the final row. 0.10 is a nice low number.


Okay, now lets make a prediction. Start by creating some test data

testData <-data.frame (Sepal.Length = 1, Sepal.Width = 4, Petal.Length =1.2, 
+ Petal.Width=0.3)

Now let’s predict

predict(fit, testData, type="class")

Here is the output:


As you can see, the model predicted setosa. If you look back at the tree, you will see why.

Let’s do one more prediction

predict (fit, newdata, type="class")

Here is the output


The model predicts 1,2,3 are virginica and 4 is versicolor.

Now go find some more data and try this out.