Text-Mining-DataCamp-Text Mining: Bag of Words

1. Jumping into Text Mining with Bag of Words

1.1 What is text mining? (video)

1.2 Understanding text mining

1.3 Quick taste of text mining

Instruction:
We’ve created an object in your workspace called new_text containing several sentences.

• Load the qdap package.
• Print new_text to the console.
• Create term_count consisting of the 10 most frequent terms in new_text.
• Plot a bar chart with the results of term_count.

# Load qdap
library(qdap)

# Print new_text to the console
new_text

# Find the 10 most frequent terms: term_count
term_count <- freq_terms(new_text, 10)

# Plot term_count
plot(term_count)

1.4 Getting started (video)

1.5 Load some text

The data has been loaded for you and is available in coffee_data_file.

• Create a new object tweets using read.csv() on the file coffee_data_file, which contains tweets mentioning coffee. Remember to add stringsAsFactors = FALSE!
• Examine the tweets object using str() to determine which column has the text you’ll want to analyze.
• Make a new coffee_tweets object using only the text column you identified earlier. To do so, use the $ operator and column name.

Instruction:

# Import text data
tweets <- read.csv('https://assets.datacamp.com/production/course_935/datasets/coffee.csv', stringsAsFactors = F)

# View the structure of tweets
str(tweets)

# Isolate text from tweets: coffee_tweets
coffee_tweets <- tweets$text

1.6 Make the vector a VCorpus object (1)

Instruction:

• Load the tm package.
• Create a Source object from the coffee_tweets vector. Call this new object coffee_source.

# Load tm
library(tm)

# Make a vector source from coffee_tweets
coffee_source <- VectorSource(coffee_tweets)

1.7 Make the vector a VCorpus object (2)

Instruction:

• Call the VCorpus() function on the coffee_source object to create coffee_corpus.
• Verify coffee_corpus is a VCorpus object by printing it to the console.
• Print the 15th element of coffee_corpus to the console to verify that it’s a PlainTextDocument that contains the content and metadata of the 15th tweet. Use double bracket subsetting.
• Print the content of the 15th tweet in coffee_corpus. Use double brackets to select the proper tweet, followed by single brackets to extract the content of that tweet.
• Print the content() of the 10th tweet within coffee_corpus

## coffee_source is already in your workspace

# Make a volatile corpus: coffee_corpus
coffee_corpus <- VCorpus(coffee_source)

# Print out coffee_corpus
coffee_corpus

# Print the 15th tweet in coffee_corpus
coffee_corpus[[15]]

# Print the contents of the 15th tweet in coffee_corpus
coffee_corpus[[15]]$content

# Now use content to review plain text of the 10th tweet
content(coffee_corpus[[10]])

1.8 Make a VCorpus from a data frame

Instruction:
In your workspace, there’s a simple data frame called example_text with the correct column names and some metadata. There is also vec_corpus which is a volatile corpus made with VectorSource()

• Create df_source using DataframeSource() with the example_text.
• Create df_corpus by converting df_source to a volatile corpus object with VCorpus().
• Print out df_corpus. Notice how many documents it contains and the number of retained document level metadata points.
• Use meta() on df_corpus to print the document associated metadata.
• Examine the pre-loaded vec_corpus object. Compare the number of documents to df_corpus.
• Use meta() on vec_corpus to compare any metadata found between vec_corpus and df_corpus.

# Create a DataframeSource from the example text
df_source <- DataframeSource(example_text)

# Convert df_source to a volatile corpus
df_corpus <- VCorpus(df_source)

# Examine df_corpus
df_corpus

# Examine df_corpus metadata
meta(df_corpus)

# Compare the number of documents in the vector source
vec_corpus

# Compare metadata in the vector corpus
meta(vec_corpus)

1.9 Cleaning and preprocessing text (video)

1.10 Common cleaning functions from tm

Instruction:
Apply each of the following functions to text, simply printing results to the console:

• tolower()
• removePunctuation()
• removeNumbers()
• stripWhitespace()

# Create the object: text
text <- "<b>She</b> woke up at       6 A.M. It\'s so early!  She was only 10% awake and began drinking coffee in front of her computer."

# Make lowercase
tolower(text)

# Remove punctuation
removePunctuation(text)

# Remove numbers
removeNumbers(text)

# Remove whitespace
stripWhitespace(text)

1.11 Cleaning with qdap

Instruction:
Apply the following functions to the text object from the previous exercise:

• bracketX()
• replace_number()
• replace_abbreviation()
• replace_contraction()
• replace_symbol()

## text is still loaded in your workspace

# Remove text within brackets
bracketX(text)

# Replace numbers with words
replace_number(text)

# Replace abbreviations
replace_abbreviation(text)

# Replace contractions
replace_contraction(text)

# Replace symbols with words
replace_symbol(text)

1.12 All about stop words

Instruction:

• Review standard stop words by calling stopwords("en").
• Remove “en” stopwords from text.
• Add “coffee” and “bean” to the standard stop words, assigning to new_stops.
• Remove the customized stopwords, new_stops, from text.

## text is preloaded into your workspace

# List standard English stop words
stopwords("en")

# Print text without standard stop words
removeWords(text, stopwords("en"))

# Add "coffee" and "bean" to the list: new_stops
new_stops <- c("coffee", "bean", stopwords("en"))

# Remove stop words from text
removeWords(text, new_stops)

1.13 Intro to word stemming and stem completion

Instruction:

• Create a vector called complicate consisting of the words “complicated”, “complication”, and “complicatedly” in that order.
• Store the stemmed version of complicate to an object called stem_doc.
• Create comp_dict that contains one word, “complicate”.
• Create complete_text by applying stemCompletion() to stem_doc. Re-complete the words using comp_dict as the reference corpus.
• Print complete_text to the console.

# Create complicate
complicate <- c("complicated", "complication", "complicatedly")

# Perform word stemming: stem_doc
stem_doc <- stemDocument(complicate)

# Create the completion dictionary: comp_dict
comp_dict <- c("complicate")

# Perform stem completion: complete_text 
complete_text <- stemCompletion(stem_doc, comp_dict)

# Print complete_text
complete_text

1.14 Word stemming and stem completion on a sentence

Instruction:
The document text_data and the completion dictionary comp_dict are loaded in your workspace.

• Remove the punctuation marks in text_data using removePunctuation(), assigning to rm_punc.
• Call strsplit() on rm_punc with the split argument set equal to " ". Nest this inside unlist(), assigning to n_char_vec.
• Use stemDocument() again to perform word stemming on n_char_vec, assigning to stem_doc.
• Create complete_doc by re-completing your stemmed document with stemCompletion() and using comp_dict as your reference corpus.

Are stem_doc and complete_doc what you expected?

# Remove punctuation: rm_punc
rm_punc <- removePunctuation(text_data)

# Create character vector: n_char_vec
n_char_vec <- unlist(strsplit(rm_punc, split = ' '))

# Perform word stemming: stem_doc
stem_doc <- stemDocument(n_char_vec) 

# Print stem_doc
stem_doc

# Re-complete stemmed document: complete_doc
complete_doc <- stemCompletion(stem_doc, comp_dict)

# Print complete_doc
complete_doc

1.15 Apply preprocessing steps to a corpus

Instruction 1:

• Edit the custom function clean_corpus() in the sample code to apply (in order):
  • tm's removePunctuation().
  • Base R’s tolower().
  • Append "mug" to the stop words list.
  • tm's stripWhitespace().

# Alter the function code to match the instructions
clean_corpus <- function(corpus) {
  # Remove punctuation
  corpus <- tm_map(corpus, removePunctuation)
  # Transform to lower case
  corpus <- tm_map(corpus, content_transformer(tolower))
  # Add more stopwords
  corpus <- tm_map(corpus, removeWords, words = c(stopwords("en"), "coffee", "mug"))
  # Strip whitespace
  corpus <- tm_map(corpus, stripWhitespace)
  return(corpus)
}

Instruction 2:

• Create clean_corp by applying clean_corpus() to the included corpus tweet_corp.
• Print the cleaned 227th tweet in clean_corp using indexing [[227]] and content().
• Compare it to the original tweet from tweets$text tweet using [227].

# Alter the function code to match the instructions
clean_corpus <- function(corpus){
  corpus <- tm_map(corpus, removePunctuation)
  corpus <- tm_map(corpus, content_transformer(tolower))
  corpus <- tm_map(corpus, removeWords, words = c(stopwords("en"), "coffee", "mug"))
  corpus <- tm_map(corpus, stripWhitespace)
  return(corpus)
}

# Apply your customized function to the tweet_corp: clean_corp
clean_corp <- clean_corpus(tweet_corp)

# Print out a cleaned up tweet
clean_corp[[227]][1]

# Print out the same tweet in original form
tweet_corp[[227]][1]

1.16 The TDM & DTM (video)

1.17 Understanding TDM and DTM

1.18 Make a document-term matrix

Instruction:

• Create coffee_dtm by applying DocumentTermMatrix() to clean_corp.
• Create coffee_m, a matrix version of coffee_dtm, using as.matrix().
• Print the dimensions of coffee_m to the console using the dim() function. Note the number of rows and columns.
• Print the subset of coffee_m containing documents (rows) 25 through 35 and terms (columns) "star" and "starbucks".

# Create the document-term matrix from the corpus
coffee_dtm <- DocumentTermMatrix(clean_corp)

# Print out coffee_dtm data
coffee_dtm

# Convert coffee_dtm to a matrix: coffee_m
coffee_m <- as.matrix(coffee_dtm)

# Print the dimensions of coffee_m
dim(coffee_m)

# Review a portion of the matrix to get some Starbucks
coffee_m[25:35, c("star", "starbucks")]

1.19 Make a term-document matrix

Instruction:

• Create coffee_tdm by applying TermDocumentMatrix() to clean_corp.
• Create coffee_m by converting coffee_tdm to a matrix using as.matrix().
• Print the dimensions of coffee_m to the console. Note the number of rows and columns.
• Print the subset of coffee_m containing terms (rows) "star" and "starbucks" and documents (columns) 25 through 35.

# Create a term-document matrix from the corpus
coffee_tdm <- TermDocumentMatrix(clean_corp)

# Print coffee_tdm data
coffee_tdm

# Convert coffee_tdm to a matrix: coffee_m
coffee_m <- as.matrix(coffee_tdm)

# Print the dimensions of the matrix
dim(coffee_m)

# Review a portion of the matrix
coffee_m[c("star", "starbucks"), 25:35]

2. Word Clouds and More Interesting Visuals

2.1 Common text mining visuals (video)

2.2 Test your understanding of text mining

2.3 Frequent terms with tm

Instruction:

• Create coffee_m as a matrix using the term-document matrix coffee_tdm from the last chapter.
• Create term_frequency using the rowSums() function on coffee_m.
• Sort term_frequency in descending order and store the result in term_frequency.
• Use single square bracket subsetting, i.e. using only one [, to print the top 10 terms from term_frequency.
• Make a barplot of the top 10 terms.

## coffee_tdm is still loaded in your workspace

# Convert coffee_tdm to a matrix
coffee_m <- as.matrix(coffee_tdm)

# Calculate the row sums of coffee_m
term_frequency <- rowSums(coffee_m)

# Sort term_frequency in decreasing order
term_frequency <- sort(term_frequency, decreasing = T)

# View the top 10 most common words
term_frequency[1:10]

# Plot a barchart of the 10 most common words
barplot(term_frequency[1:10], col = "tan", las = 2)

2.4 Frequent terms with qdap

Instruction 1:

• Create frequency using the freq_terms() function on tweets$text. Include arguments to accomplish the following:
  • Limit to the top 10 terms.
  • At least 3 letters per term.
  • Use "Top200Words" to define stop words.
• Produce a plot() of the frequency object. Compare it to the plot you produced in the previous exercise.

# Create frequency
frequency <- freq_terms(
  tweets$text, 
  top = 10, 
  at.least = 3, 
  stopwords = "Top200Words"
)

# Make a frequency barchart
plot(frequency)

Instruction 2:

• Again, create frequency using the freq_terms() function on tweets$text. Include the following arguments:
  • Limit to the top 10 terms.
  • At least 3 letters per term.
  • This time use stopwords("english") to define stop words.
• Produce a plot() of frequency. Compare it to the plot of frequency. Do certain words change based on the stop words criterion?

# Create frequency
frequency <- freq_terms(tweets$text,
  top = 10,
  at.least = 3, 
  stopwords = stopwords("english")
  )

# Make a frequency barchart
plot(frequency)

2.5 Intro to word clouds (video)

2.6 A simple word cloud

Instruction:

• Load the wordcloud package.
• Print out first 10 entries in term_frequency.
• Extract the terms using names() on term_frequency. Call the vector of strings terms_vec.
• Create a wordcloud() using terms_vec as the words, and term_frequency as the values. Add the parameters max.words = 50 and colors = "red".

# Load wordcloud package
library(wordcloud)

# Print the first 10 entries in term_frequency
term_frequency[1:10]

# Vector of terms
terms_vec <- names(term_frequency)


# Create a wordcloud for the values in word_freqs
wordcloud(terms_vec, term_frequency, max.words = 50, colors = "red")

2.7 Stop words and word clouds

Instruction:

• Apply content() to the 24th document in chardonnay_corp.
• Append "chardonnay" to the English stopwords, assigning to stops.
• Examine the last 6 words in stops.
• Create cleaned_chardonnay_corp with tm_map() by passing in the chardonnay_corp, the function removeWords and finally the stopwords, stops.
• Now examine the content of the 24 tweet again to compare results.

# Review a "cleaned" tweet
content(chardonnay_corp[[24]])

# Add to stopwords
stops <- c(stopwords(kind = 'en'), 'chardonnay')

# Review last 6 stopwords 
tail(stops)

# Apply to a corpus
cleaned_chardonnay_corp <- tm_map(chardonnay_corp, removeWords, stops)

# Review a "cleaned" tweet again
content(cleaned_chardonnay_corp[[24]])

2.8 Plot the better word cloud

Instruction:

We’ve loaded the wordcloud package for you behind the scenes and will do so for all additional exercises requiring it.

• Sort the values in chardonnay_words with decreasing = TRUE. Save as sorted_chardonnay_words.
• Look at the top 6 words in sorted_chardonnay_words and their values.
• Create terms_vec using names() on chardonnay_words.
• Pass terms_vec and chardonnay_words into the wordcloud() function. Review what other words pop out now that “chardonnay” is removed.

# Sort the chardonnay_words in descending order
sorted_chardonnay_words <- sort(chardonnay_words, decreasing = TRUE)

# Print the 6 most frequent chardonnay terms
head(sorted_chardonnay_words)

# Get a terms vector
terms_vec <- names(chardonnay_words)

# Create a wordcloud for the values in word_freqs
wordcloud(terms_vec, chardonnay_words, max.words = 50, colors = "red")

2.9 Improve word cloud colors

Instruction:

• Call the colors() function to list all basic colors.
• Create a wordcloud() using the predefined chardonnay_freqs with the colors “grey80”, “darkgoldenrod1”, and “tomato”. Include the top 100 terms using max.words.

# Print the list of colors
colors()

# Print the wordcloud with the specified colors
wordcloud(chardonnay_freqs$term, 
          chardonnay_freqs$num,
          max.words = 100, 
          colors = c("grey80","darkgoldenrod1", "tomato"))

2.10 Use prebuilt color palettes

Instruction:

• Use cividis() to select 5 colors in an object called color_pal.
• Review the hexadecimal colors by printing color_pal to your console.
• Create a wordcloud() from the chardonnay_freqs term and num columns. Include the top 100 terms using max.words, and set the colors to your palette, color_pal.

# Select 5 colors 
color_pal <- cividis(5)

# Examine the palette output
color_pal

# Create a wordcloud with the selected palette
wordcloud(chardonnay_freqs$term, 
          chardonnay_freqs$num,
          max.words = 100, 
          colors = color_pal)

2.11 Other word clouds and word networks (video)

2.12 Find common words

Instruction:

• Create all_coffee by using paste() with collapse = " " on coffee_tweets$text.
• Create all_chardonnay by using paste() with collapse = " " on chardonnay_tweets$text.
• Create all_tweets using c() to combine all_coffee and all_chardonnay. Make all_coffee the first term.
• Convert all_tweets using VectorSource().
• Create all_corpus by using VCorpus() on all_tweets.

# Create all_coffee
all_coffee=paste(coffee_tweets$text,collapse=" ")

# Create all_chardonnay
all_chardonnay=paste(chardonnay_tweets$text,collapse=" ")

# Create all_tweets
all_tweets=c(all_coffee,all_chardonnay)

# Convert to a vector source
all_tweets=VectorSource(all_tweets)

# Create all_corpus
all_corpus=VCorpus(all_tweets)

2.13 Visualize common words

Instruction:

• Create all_clean by applying the predefined clean_corpus() function to all_corpus.
• Create all_tdm, a TermDocumentMatrix from all_clean.
• Create all_m by converting all_tdm to a matrix object.
• Create a commonality.cloud() from all_m with max.words = 100 and colors = "steelblue1".

# Clean the corpus
all_clean <- clean_corpus(all_corpus)

# Create all_tdm
all_tdm <- TermDocumentMatrix(all_clean)

# Create all_m
all_m <- as.matrix(all_tdm)

# Print a commonality cloud
commonality.cloud(all_m, max.words = 100, colors = "steelblue1")

2.14 Visualize dissimilar words

Instruction:

all_corpus is preloaded in your workspace.

• Create all_clean by applying the predefined clean_corpus function to all_corpus.
• Create all_tdm, a TermDocumentMatrix, from all_clean.
• Use colnames() to rename each distinct corpora within all_tdm. Name the first column “coffee” and the second column “chardonnay”.
• Create all_m by converting all_tdm into matrix form.
• Create a comparison.cloud() using all_m, with colors = c("orange", "blue") and max.words = 50.

# Clean the corpus
all_clean <- clean_corpus(all_corpus)

# Create all_tdm
all_tdm <- TermDocumentMatrix(all_clean)

# Give the columns distinct names
colnames(all_tdm) <- c("coffee", "chardonnay")

# Create all_m
all_m <- as.matrix(all_tdm)

# Create comparison cloud
comparison.cloud(all_m,
                 colors = c("orange", "blue"),
                 max.words = 50)

2.15 Polarized tag cloud

Instruction 1:

• Convert all_tdm_m to a data frame. Set the rownames to a column named "word".
• Filter to keep rows where all columns are greater than zero, using the syntax . > 0.
• Add a column named difference, equal to the count in the chardonnay column minus the count in the coffee column.
• Filter to keep the top 25 rows by difference.
• Arrange the rows by desc()ending order of difference.

top25_df <- all_tdm_m %>%
  # Convert to data frame
  as_data_frame(rownames = "word") %>% 
  # Keep rows where word appears everywhere
  filter_all(all_vars(. > 0)) %>% 
  # Get difference in counts
  mutate(difference = chardonnay - coffee) %>% 
  # Keep rows with biggest difference
  top_n(25, wt = difference) %>% 
  # Arrange by descending difference
  arrange(desc(difference))

Instruction 2:

• Set the left count to the chardonnay column.
• Set the right count to the coffee column.
• Set the labels to the word column.

top25_df <- all_tdm_m %>%
  # Convert to data frame
  as_data_frame(rownames = "word") %>% 
  # Keep rows where word appears everywhere
  filter_all(all_vars(. > 0)) %>% 
  # Get difference in counts
  mutate(difference = chardonnay - coffee) %>% 
  # Keep rows with biggest difference
  top_n(25, wt = difference) %>% 
  # Arrange by descending difference
  arrange(desc(difference))
  
pyramid.plot(
  # Chardonnay counts
  top25_df$chardonnay, 
  # Coffee counts
  top25_df$coffee, 
  # Words
  labels = top25_df$word, 
  top.labels = c("Chardonnay", "Words", "Coffee"), 
  main = "Words in Common", 
  unit = NULL,
  gap = 8,
)

2.16 Visualize word networks

Instruction:

Update the word_associate() plotting code to work with the coffee data.

• Change the vector to coffee_tweets$text.
• Change the match string to "barista".
• Change "chardonnay" to "coffee" in the stopwords too.
• Change the title to "Barista Coffee Tweet Associations" in the sample code for the plot.

# Word association
word_associate(coffee_tweets$text, match.string = "barista", 
               stopwords = c(Top200Words, "coffee", "amp"), 
               network.plot = TRUE, cloud.colors = c("gray85", "darkred"))

# Add title
title(main = "Barista Coffee Tweet Associations")

2.17 Teaser simple word clustering

Instruction:

A hierarchical cluster object, hc, has been created for you from the coffee tweets.

Create a dendrogram using plot() on hc.

# Plot a dendrogram
plot(hc)

3. Adding to Your tm Skills

3.1 Simple word clustering (video)

3.2 Test your understanding of text mining

3.3 Distance matrix and dendrogram

Instruction:
The data frame rain has been preloaded in your workspace.

• Create dist_rain by using the dist() function on the values in the second column of rain.
• Print the dist_rain matrix to the console.
• Create hc by performing a cluster analysis, using hclust() on dist_rain.
• plot() the hc object with labels = rain$city to add the city names.

# Create dist_rain
dist_rain <- dist(rain[, 2])

# View the distance matrix
dist_rain

# Create hc
hc <- hclust(dist_rain)

# Plot hc
plot(hc, labels = rain$city)

3.4 Make a dendrogram friendly TDM

Instruction:

tweets_tdm has been created using the chardonnay tweets.

• Print the dimensions of tweets_tdm to the console.
• Create tdm1 using removeSparseTerms() with sparse = 0.95 on tweets_tdm.
• Create tdm2 using removeSparseTerms() with sparse = 0.975 on tweets_tdm.
• Print tdm1 to the console to see how many terms are left.
• Print tdm2 to the console to see how many terms are left.

# Print the dimensions of tweets_tdm
dim(tweets_tdm)

# Create tdm1
tdm1 <- removeSparseTerms(tweets_tdm, sparse = 0.95)

# Create tdm2
tdm2 <- removeSparseTerms(tweets_tdm, sparse = 0.975)

# Print tdm1
tdm1

# Print tdm2
tdm2

3.5 Put it all together: a tea based dendrogram

Instruction:

• Create tweets_tdm2 by applying removeSparseTerms() on tweets_tdm. Use sparse = 0.975.
• Create tdm_m by using as.matrix() on tweets_tdm2 to convert it to matrix form.
• Create tweets_dist containing the distances of tdm_m using the dist() function.
• Create a hierarchical cluster object called hc using hclust() on tweets_dist.
  Make a dendrogram with plot() and hc.

# Create tweets_tdm2
tweets_tdm2 <- removeSparseTerms(tweets_tdm, sparse = 0.975)

# Create tdm_m
tdm_m <- as.matrix(tweets_tdm2)

# Create tweets_dist
tweets_dist <- dist(tdm_m)

# Create hc
hc <- hclust(tweets_dist)

# Plot the dendrogram
plot(hc)

3.6 Dendrogram aesthetics

Instruction:

The dendextend package has been loaded for you, and a hierarchical cluster object, hc, was created from tweets_dist.

• Create hcd as a dendrogram using as.dendrogram() on hc.
• Print the labels of hcd to the console.
• Use branches_attr_by_labels() to color the branches. Pass it three arguments: the hcd object, c("marvin", "gaye"), and the color "red". Assign to hcd_colored.
• plot() the dendrogram hcd_colored with the title "Better Dendrogram", added using the main argument.
• Add rectangles to the plot using rect.dendrogram(). Specify k = 2 clusters and a border color of "grey50".

# Create hcd
hcd <- as.dendrogram(hc)

# Print the labels in hcd
labels(hcd)

# Change the branch color to red for "marvin" and "gaye"
hcd_colored <- branches_attr_by_labels(hcd, c("marvin", "gaye"), color = "red")

# Plot hcd
plot(hcd_colored, main = "Better Dendrogram")

# Add cluster rectangles 
rect.dendrogram(hcd_colored, k = 2, border = "grey50")

3.7 Using word association

Instruction:

• Create associations using findAssocs() on tweets_tdm to find terms associated with “venti”, which meet a minimum threshold of 0.2.
• View the terms associated with “venti” by printing associations to the console.
• Create associations_df, by calling list_vect2df(), passing associations, then setting col2 to "word" and col3 to "score".
• Run the ggplot2 code to make a dot plot of the association values.

# Create associations
associations <- findAssocs(tweets_tdm, "venti", 0.2)

# View the venti associations
associations

# Create associations_df
associations_df <- list_vect2df(associations, col2 = "word", col3 = "score")

# Plot the associations_df values
ggplot(associations_df, aes(score, word)) + 
  geom_point(size = 3) + 
  theme_gdocs()

3.8 Getting past single words (video)

3.9 N-gram tokenization

3.10 Changing n-grams

Instruction:

A corpus has been preprocessed as before using the chardonnay tweets. The resulting object text_corp is available in your workspace.

• Create a tokenizer function like the above which creates 2-word bigrams.
• Make unigram_dtm by calling DocumentTermMatrix() on text_corp without using the tokenizer() function.
• Make bigram_dtm using DocumentTermMatrix() on text_corp with the tokenizer() function you just made.
• Examine unigram_dtm and bigram_dtm. Which has more terms?

# Make tokenizer function 
tokenizer <- function(x)
  NGramTokenizer(x, Weka_control(min = 2, max = 2))

# Create unigram_dtm
unigram_dtm <- DocumentTermMatrix(text_corp)

# Create bigram_dtm
bigram_dtm <- DocumentTermMatrix(
  text_corp,
  control = list(tokenize = tokenizer))

# Print unigram_dtm
unigram_dtm

# Print bigram_dtm
bigram_dtm

3.11 How do bigrams affect word clouds?

Instruction:
The chardonnay tweets have been cleaned and organized into a DTM called bigram_dtm.

• Create bigram_dtm_m by converting bigram_dtm to a matrix.
• Create an object freq consisting of the word frequencies by applying colSums() on bigram_dtm_m.
• Extract the character vector of word combinations with names(freq) and assign the result to bi_words.
• Pass bi_words to str_subset() with the matching pattern "^marvin" to review all bigrams starting with “marvin”.
• Plot a simple wordcloud() passing bi_words, freq and max.words = 15 into the function.

# Create bigram_dtm_m
bigram_dtm_m <- as.matrix(bigram_dtm)

# Create freq
freq <- colSums(bigram_dtm_m)

# Create bi_words
bi_words <- names(freq)

# Examine part of bi_words
str_subset(bi_words, pattern = "^marvin")

# Plot a wordcloud
wordcloud(bi_words, freq, max.words = 15)

3.12 Different frequency criteria (video)

3.13 Changing frequency weights

Instruction 1:

• Create tdm, a term frequency-based TermDocumentMatrix() using text_corp.
• Create tdm_m by converting tdm to matrix form.
• Examine the term frequency for “coffee”, “espresso”, and “latte” in a few tweets. Subset tdm_m to get rows c("coffee", "espresso", "latte") and columns 161 to 166.

# Create a TDM
tdm <- TermDocumentMatrix(text_corp)

# Convert it to a matrix
tdm_m <- as.matrix(tdm)

# Examine part of the matrix
tdm_m[c("coffee", "espresso", "latte"), 161:166]

Instruction 2:

• Edit the TermDocumentMatrix() to use TfIdf weighting. Pass control = list(weighting = weightTfIdf) as an argument to the function.
• Run the code and compare the new scores to the first part of the exercise.

# Edit the controls to use Tfidf weighting
tdm <- TermDocumentMatrix(text_corp, 
control = list(weighting = weightTfIdf))

# Convert to matrix again
tdm_m <- as.matrix(tdm)

# Examine the same part: how has it changed?
tdm_m[c("coffee", "espresso", "latte"), 161:166]

3.14 Capturing metadata in tm

Instruction:

• Rename the first column of tweets to “doc_id”.
• Set the document schema with DataframeSource() on the smaller tweets data frame.
• Make the document collection a volatile corpus nested in the custom clean_corpus() function.
• Apply content() to the first tweet with double brackets such as text_corpus[[1]] to see the cleaned plain text.
• Confirm that all metadata was captured using the meta() function on the first document with single brackets.

Remember, when accessing part of a corpus the double or single brackets make a difference! For this exercise you will use double brackets with content() and single brackets with meta().

# Rename columns
names(tweets)[1] <- "doc_id"

# Set the schema: docs
docs <- DataframeSource(tweets)

# Make a clean volatile corpus: text_corpus
text_corpus <- clean_corpus( VCorpus(docs))

# Examine the first doc content
content(text_corpus[[1]])

# Access the first doc metadata
meta(text_corpus[1])

4. Battle of the Tech Giants for Talent

4.1 Amazon vs. Google (video)

4.2 Organizing a text mining project

4.3 Step 1: Problem definition

4.4 Step 2: Identifying the text sources

Instruction:

• View the structure of amzn with str() to get its dimensions and a preview of the data.
• Create amzn_pros from the positive reviews column amzn$pros.
• Create amzn_cons from the negative reviews column amzn$cons.
• Print the structure of goog with str() to get its dimensions and a preview of the data.
• Create goog_pros from the positive reviews column goog$pros.
• Create goog_cons from the negative reviews column goog$cons.

# Print the structure of amzn
str(amzn)

# Create amzn_pros
amzn_pros <- amzn$pros

# Create amzn_cons
amzn_cons <- amzn$cons

# Print the structure of goog
str(goog)

# Create goog_pros
goog_pros <- goog$pros

# Create goog_cons
goog_cons <- goog$cons

4.5 Step 3:Text organization (video)

4.6 Text organization

Instruction 1:

• Apply qdap_clean() to amzn_pros, assigning to qdap_cleaned_amzn_pros.
• Create a vector source (VectorSource()) from qdap_cleaned_amzn_pros, then turn it into a volatile corpus (VCorpus()), assigning to amzn_p_corp.
• Create amzn_pros_corp by applying tm_clean() to amzn_p_corp.

# qdap_clean the text
qdap_cleaned_amzn_pros <- qdap_clean(amzn_pros)

# Source and create the corpus
amzn_p_corp <- VCorpus(VectorSource(qdap_cleaned_amzn_pros))

# tm_clean the corpus
amzn_pros_corp <- tm_clean(amzn_p_corp)

Instruction 2:

• Apply qdap_clean() to amzn_cons, assigning to qdap_cleaned_amzn_cons.
• Create a vector source from qdap_cleaned_amzn_cons, then turn it into a volatile corpus, assigning to amzn_c_corp.
• Create amzn_cons_corp by applying tm_clean() to amzn_c_corp.

# qdap_clean the text
qdap_cleaned_amzn_cons <- qdap_clean(amzn_cons)

# Source and create the corpus
amzn_c_corp <- VCorpus(VectorSource(qdap_cleaned_amzn_cons))

# tm_clean the corpus
amzn_cons_corp <- tm_clean(amzn_c_corp)

4.7 Working with Google reviews

Instruction 1:

• Apply qdap_clean() to goog_pros, assigning to qdap_cleaned_goog_pros.
• Create a vector source (VectorSource()) from qdap_cleaned_goog_pros, then turn it into a volatile corpus (VCorpus()), assigning to goog_p_corp.
• Create goog_pros_corp by applying tm_clean() to goog_p_corp.

# qdap_clean the text
qdap_cleaned_goog_pros <- qdap_clean(goog_pros)

# Source and create the corpus
goog_p_corp <- VCorpus(VectorSource(qdap_cleaned_goog_pros))

# tm_clean the corpus
goog_pros_corp <- tm_clean(goog_p_corp)

Instruction 2:

• Apply qdap_clean() to goog_cons, assigning to qdap_cleaned_goog_cons.
• Create a vector source from qdap_cleaned_goog_cons, then turn it into a volatile corpus, assigning to goog_c_corp.
• Create goog_cons_corp by applying tm_clean() to goog_c_corp.

# qdap clean the text
qdap_cleaned_goog_cons <- qdap_clean(goog_cons)

# Source and create the corpus
goog_c_corp <- VCorpus(VectorSource(qdap_cleaned_goog_cons))

# tm clean the corpus
goog_cons_corp <- tm_clean(goog_c_corp)

4.8 Steps 4 & 5: Feature extraction & analysis

4.9 Feature extraction & analysis: amzn_pros

Instruction:

• Create amzn_p_tdm as a TermDocumentMatrix from amzn_pros_corp. Make sure to add control = list(tokenize = tokenizer) so that the terms are bigrams.
• Create amzn_p_tdm_m from amzn_p_tdm by using the as.matrix() function.
• Create amzn_p_freq to obtain the term frequencies from amzn_p_tdm_m.
• Create a wordcloud() using names(amzn_p_freq) as the words, amzn_p_freq as their frequencies, and max.words = 25 and color = "blue" for aesthetics.

# Create amzn_p_tdm
amzn_p_tdm <- TermDocumentMatrix(
  amzn_pros_corp, 
  control = list(tokenize = tokenizer)
  )

# Create amzn_p_tdm_m
amzn_p_tdm_m <- as.matrix(amzn_p_tdm)

# Create amzn_p_freq
amzn_p_freq <- rowSums(amzn_p_tdm_m)

# Plot a wordcloud using amzn_p_freq values
wordcloud(names(amzn_p_freq),
  amzn_p_freq,
  max.words = 25, 
  color = "blue")

4.10 Feature extraction & analysis: amzn_cons

Instruction:

Create amzn_c_tdm by converting amzn_cons_corp into a TermDocumentMatrix and incorporating the bigram function control = list(tokenize = tokenizer).
Create amzn_c_tdm_m as a matrix version of amzn_c_tdm.
Create amzn_c_freq by using rowSums() to get term frequencies from amzn_c_tdm_m.
Create a wordcloud() using names(amzn_c_freq) and the values amzn_c_freq. Use the arguments max.words = 25 and color = "red" as well.

# Create amzn_c_tdm
amzn_c_tdm <- TermDocumentMatrix(
  amzn_cons_corp,
  control = list(tokenize = tokenizer))

# Create amzn_c_tdm_m
amzn_c_tdm_m <- as.matrix(amzn_c_tdm)

# Create amzn_c_freq
amzn_c_freq <- rowSums(amzn_c_tdm_m)

# Plot a wordcloud of negative Amazon bigrams
wordcloud(names(amzn_c_freq), amzn_c_freq,
  max.words = 25, color = "red")

4.11 amzn_cons dendrogram

Instruction:

• Create amzn_c_tdm as a TermDocumentMatrix using amzn_cons_corp with control = list(tokenize = tokenizer).
• Print amzn_c_tdm to the console.
• Create amzn_c_tdm2 by applying the removeSparseTerms() function to amzn_c_tdm with the sparse argument equal to .993.
• Create hc, a hierarchical cluster object by nesting the distance matrix dist(amzn_c_tdm2) inside the hclust() function. Make sure to also pass method = "complete" to the hclust() function.
• Plot hc to view the clustered bigrams and see how the concepts in the Amazon cons section may lead you to a conclusion.

# Create amzn_c_tdm
amzn_c_tdm <- TermDocumentMatrix(
  amzn_cons_corp, 
  control = list(tokenize = tokenizer))

# Print amzn_c_tdm to the console
amzn_c_tdm

# Create amzn_c_tdm2 by removing sparse terms 
amzn_c_tdm2 <- removeSparseTerms(amzn_c_tdm, sparse = .993)

# Create hc as a cluster of distance values
hc <- hclust(
  d = dist(amzn_c_tdm2), method = "complete")

# Produce a plot of hc
plot(hc)

4.12 Word association

Instruction:
The amzn_pros_corp corpus has been cleaned using the custom functions like before.

Construct a TDM called amzn_p_tdm from amzn_pros_corp and control = list(tokenize = tokenizer).
Create amzn_p_m by converting amzn_p_tdm to a matrix.
Create amzn_p_freq by applying rowSums() to amzn_p_m.
Create term_frequency using sort() on amzn_p_freq along with the argument decreasing = TRUE.
Examine the first 5 bigrams using term_frequency[1:5].
You may be surprised to see “fast paced” as a top term because it could be a negative term related to “long hours”. Look at the terms most associated with “fast paced”. Use findAssocs() on amzn_p_tdm to examine "fast paced" with a 0.2 cutoff.

# Create amzn_p_tdm
amzn_p_tdm <- TermDocumentMatrix(
  amzn_pros_corp,
  control = list(tokenize = tokenizer)
)

# Create amzn_p_m
amzn_p_m <- as.matrix(amzn_p_tdm)

# Create amzn_p_freq
amzn_p_freq <- rowSums(amzn_p_m)

# Create term_frequency
term_frequency <- sort(amzn_p_freq, decreasing = T)

# Print the 5 most common terms
term_frequency[1:5]

# Find associations with fast paced
associations <- findAssocs(amzn_p_tdm, "fast paced", 0.2)
associations

4.13 Quick review of Google reviews

Instruction:
The all_goog_corpus object consisting of Google pro and con reviews is loaded in your workspace.

Create all_goog_corp by cleaning all_goog_corpus with the predefined tm_clean() function.
Create all_tdm by converting all_goog_corp to a term-document matrix.
Create all_m by converting all_tdm to a matrix.
Construct a comparison.cloud() from all_m. Set max.words to 100. The colors argument is specified for you.

# Create all_goog_corp
all_goog_corp <- tm_clean(all_goog_corpus)

# Create all_tdm
all_tdm <- TermDocumentMatrix(all_goog_corp)

# Create all_m
all_m <- as.matrix(all_tdm)

# Build a comparison cloud
comparison.cloud(all_m,
  colors = c("#F44336", "#2196f3"),
  max.words = 100)

4.14 Cage match! Amazon vs. Google pro reviews

Instruction:

• Create common_words from all_tdm_df using dplyr functions.
  • filter() on the AmazonPro column for nonzero values.
  • Likewise filter the GooglePro column for nonzero values.
  • Then mutate() a new column, diff which is the abs (absolute) difference between the term frequencies columns.
• Although we could have piped again, create top5_df by applying top_n to common_words to extract the top 5 values in the diff column. It will print to your console for review.
• Create a pyramid.plot passing in top5_df$AmazonPro then top5_df$GooglePro and finally add labels with top5_df$terms.

# Filter to words in common and create an absolute diff column
common_words <- all_tdm_df %>% 
  filter(
    AmazonPro != 0,
    GooglePro != 0
  ) %>%
  mutate(diff = abs(AmazonPro - GooglePro))

# Extract top 5 common bigrams
(top5_df <- top_n(common_words, 5))

# Create the pyramid plot
pyramid.plot(top5_df$AmazonPro, top5_df$GooglePro, 
             labels = top5_df$terms, gap = 12, 
             top.labels = c("Amzn", "Pro Words", "Goog"), 
             main = "Words in Common", unit = NULL)

4.15 Cage match, part 2! Negative reviews

Instruction:

• Using top_n() on common_words, obtain the top 5 bigrams weighted on the diff column. The results of the new object will print to your console.
• Create a pyramid.plot(). Pass in top5_df$AmazonNeg, top5_df$GoogleNeg, and labels = top5_df$terms. For better labeling, set
  • gap to 12.
  • top.labels to c("Amzn", "Neg Words", "Goog")

The main and unit arguments are set for you.

# Extract top 5 common bigrams
(top5_df <- top_n(common_words, 5, wt = diff))

# Create a pyramid plot
pyramid.plot(
    # Amazon on the left
    top5_df$AmazonNeg,
    # Google on the right
    top5_df$GoogleNeg,
    # Use terms for labels
    labels = top5_df$terms,
    # Set the gap to 12
    gap = 12,
    # Set top.labels to "Amzn", "Neg Words" & "Goog"
    top.labels = c("Amzn", "Neg Words", "Goog"),
    main = "Words in Common", 
    unit = NULL
)

4.16 Step 6: Reach a conclusion (video)

4.17 Draw conclusions, insights, or recommendations

Instruction:

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4.18 Draw another conclusion, insight, or recommendation

Instruction:

在这里插入代码片

4.19 Finished!