cozyDS

The source codes and contens come from the E-Learning DataCamp: Sentiment Analysis in R: The Tidy Way Enjoy

Intro

• Text datasets are diverse and ubiquitous, and sentiment analysis provides an approach to understand the attitudes and opinions expressed in these texts. In this course, you will develop your text mining skills using tidy data principles. You will apply these skills by performing sentiment analysis in several case studies, on text data from Twitter to TV news to Shakespeare. These case studies will allow you to practice important data handling skills, learn about the ways sentiment analysis can be applied, and extract relevant insights from real-world data.

1. Sentiment Lexicons

There are several different sentiment lexicons available for sentiment analysis. You will explore three in this course that are available in the tidytext package: + afinn from Finn Årup Nielsen, + bing from Bing Liu and collaborators, and + nrc from Saif Mohammad and Peter Turney. You will see how these lexicons can be used as you work through this course. The decision about which lexicon to use often depends on what question you are trying to answer.

# Load dplyr and tidytext
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidytext)

# Choose the bing lexicon
get_sentiments("bing")

## # A tibble: 6,788 x 2
##    word        sentiment
##    <chr>       <chr>    
##  1 2-faced     negative 
##  2 2-faces     negative 
##  3 a+          positive 
##  4 abnormal    negative 
##  5 abolish     negative 
##  6 abominable  negative 
##  7 abominably  negative 
##  8 abominate   negative 
##  9 abomination negative 
## 10 abort       negative 
## # ... with 6,778 more rows

# Choose the nrc lexicon
get_sentiments("nrc") %>%
  count(sentiment) # Count words by sentiment

## # A tibble: 10 x 2
##    sentiment        n
##    <chr>        <int>
##  1 anger         1247
##  2 anticipation   839
##  3 disgust       1058
##  4 fear          1476
##  5 joy            689
##  6 negative      3324
##  7 positive      2312
##  8 sadness       1191
##  9 surprise       534
## 10 trust         1231

• While the “bing” lexicon classifies words into 2 sentiments, positive or negative, there are 10 sentiments conveyed in the “nrc” lexicon.

2. Implement an inner join

In this exercise you will implement sentiment analysis using an inner join. The inner_join() function from dplyr will identify which words are in both the sentiment lexicon and the text dataset you are examining. To learn more about joining data frames using dplyr. The geocoded_tweets dataset is taken from Quartz and contains three columns:

load("data/geocoded_tweets.rda")

# Access bing lexicon: bing
bing <- get_sentiments("bing")

# Use data frame with text data
geocoded_tweets %>%
  # With inner join, implement sentiment analysis using `bing`
  inner_join(bing)

## Joining, by = "word"

## # A tibble: 64,303 x 4
##    state   word             freq sentiment
##    <chr>   <chr>           <dbl> <chr>    
##  1 alabama abuse           7186. negative 
##  2 alabama abused          3073. negative 
##  3 alabama accomplish      5957. positive 
##  4 alabama accomplished   13121. positive 
##  5 alabama accomplishment  3036. positive 
##  6 alabama accurate       28262. positive 
##  7 alabama ache            7306. negative 
##  8 alabama aching          5080. negative 
##  9 alabama addict          5441. negative 
## 10 alabama addicted       40389. negative 
## # ... with 64,293 more rows

• you can see the average frequency and the sentiment associated with each word that exists in both data frames.

3. What are the most common sadness words?

After you have implemented sentiment analysis using inner_join(), you can use dplyr functions such as group_by() and summarize() to understand your results. For example, what are the most common words related to sadness in this Twitter dataset?

tweets_nrc <- geocoded_tweets %>%
  inner_join(get_sentiments("nrc"))

## Joining, by = "word"

tweets_nrc %>% 
  filter(sentiment == "sadness") %>% 
  group_by(word) %>% 
  summarise(freq = mean(freq)) %>% 
  arrange(desc(freq))

## # A tibble: 585 x 2
##    word        freq
##    <chr>      <dbl>
##  1 hate    1253840.
##  2 bad      984943.
##  3 bitch    787774.
##  4 hell     486259.
##  5 crazy    447047.
##  6 feeling  407562.
##  7 leave    397806.
##  8 mad      393559.
##  9 music    373608.
## 10 sick     362023.
## # ... with 575 more rows

4. What are the most common joy words?

You can use the same approach from the last exercise to find the most common words associated with joy in these tweets. Use the same pattern of dplyr verbs to find a new result.

joy_words <- tweets_nrc %>%
  filter(sentiment == "joy") %>%
  group_by(word) %>%
  summarise(freq = mean(freq)) %>%
  arrange(desc(freq))    

library(ggplot2)

joy_words %>%
  top_n(20) %>%
  mutate(word = reorder(word, freq)) %>%
  ggplot(aes(x = word, y = freq)) +
  geom_col(stat = "identity") +
  coord_flip() 

## Selecting by freq

## Warning: Ignoring unknown parameters: stat

5. Do people in different states use different words?

So far you have looked at the United States as a whole, but you can use this dataset to examine differences in word use by state. In this exercise, you will examine two states and compare their use of joy words. Do they use the same words associated with joy? Do they use these words at the same rate?

tweets_nrc %>%
  filter(state == "utah",
      sentiment == "joy") %>%
  arrange(desc(freq))

## # A tibble: 326 x 4
##    state word          freq sentiment
##    <chr> <chr>        <dbl> <chr>    
##  1 utah  love      4207322. joy      
##  2 utah  good      3035114. joy      
##  3 utah  happy     1402568. joy      
##  4 utah  pretty     902947. joy      
##  5 utah  fun        764045. joy      
##  6 utah  birthday   663439. joy      
##  7 utah  beautiful  653061. joy      
##  8 utah  friend     627522. joy      
##  9 utah  hope       571841. joy      
## 10 utah  god        536687. joy      
## # ... with 316 more rows

tweets_nrc %>%
  filter(state == "louisiana",
      sentiment == "joy") %>%
    arrange(desc(freq))

## # A tibble: 290 x 4
##    state     word         freq sentiment
##    <chr>     <chr>       <dbl> <chr>    
##  1 louisiana love     3764157. joy      
##  2 louisiana good     2758699. joy      
##  3 louisiana baby     1184392. joy      
##  4 louisiana happy    1176291. joy      
##  5 louisiana god       882457. joy      
##  6 louisiana birthday  740497. joy      
##  7 louisiana money     677899. joy      
##  8 louisiana hope      675559. joy      
##  9 louisiana pretty    581242. joy      
## 10 louisiana feeling   486367. joy      
## # ... with 280 more rows

• Words like “baby” and “money” are popular in Louisiana but not in Utah.

6. Which states have the most positive Twitter users?

• you will determine how the overall sentiment of Twitter sentiment varies from state to state. You will use a dataset called tweets_bing, which is the output of an inner join created just the same way that you did earlier. Check out what tweets_bing looks like in the console.

library(tidyr)
tweets_bing <- geocoded_tweets %>% 
  inner_join(get_sentiments("bing"))

## Joining, by = "word"

tweets_bing %>% 
  group_by(state, sentiment) %>% 
  summarise(freq = mean(freq)) %>% 
  spread(sentiment, freq) %>% 
  ungroup() %>% 
  mutate(ratio = positive / negative, 
         state = reorder(state, ratio)) %>% 
  ggplot(aes(x = state, y = ratio)) + 
    geom_point() + 
    coord_flip()

• Combining your data with a sentiment lexicon, you can do all sorts of exploratory data analysis. Looks like Missouri tops the list for this one!

The source codes and contens come from the book Text Mining with R Enjoy

Intro

• Let’s address the topic of opinion mining or sentiment analysis. When human readers approach a text, we use our understanding of the emotional intent of words to infer whether a section of text is positive or negative, or perhaps characterized by some other more nuanced emotion like surprise or disgust. The flow chart is shown in Figure 2.1.

knitr::include_graphics("img/Figure_2.1_Sentiment_Analysis.png")

• One way to analyze the sentiment of a text is to consider the text as a combination of its individual words and the sentiment content of the whole text as the sum of the sentiment content of the individual words. This isn’t the only way to approach sentiment analysis, but it is an often-used approach, and an approach that naturally takes advantage of the tidy tool ecosystem.

1. The Sentiment DataSet

library(tidytext)
sentiments

## # A tibble: 27,314 x 4
##    word        sentiment lexicon score
##    <chr>       <chr>     <chr>   <int>
##  1 abacus      trust     nrc        NA
##  2 abandon     fear      nrc        NA
##  3 abandon     negative  nrc        NA
##  4 abandon     sadness   nrc        NA
##  5 abandoned   anger     nrc        NA
##  6 abandoned   fear      nrc        NA
##  7 abandoned   negative  nrc        NA
##  8 abandoned   sadness   nrc        NA
##  9 abandonment anger     nrc        NA
## 10 abandonment fear      nrc        NA
## # ... with 27,304 more rows

The three general-purpose lexicons are + AFINN from Finn Årup Nielsen, + bing from Bing Liu and collaborators, and + nrc from Saif Mohammad and Peter Turney.

Common

• All three of these lexicons are based on unigrams, i.e., single words.
• These lexicons contain many English words and the words are assigned scores for positive/negative sentiment, and also possibly emotions like joy, anger, sadness, and so forth.

Differences

• The nrc lexicon categorizes words in a binary fashion (“yes”/“no”) into categories of positive, negative, anger, anticipation, disgust, fear, joy, sadness, surprise, and trust.

• The bing lexicon categorizes words in a binary fashion into positive and negative categories.

• The AFINN lexicon assigns words with a score that runs between -5 and 5, with negative scores indicating negative sentiment and positive scores indicating positive sentiment.

• tidytext provides a function get_sentiments() to get specific sentiment lexicons without the columns that are not used in that lexicon.

get_sentiments("afinn")

## # A tibble: 2,476 x 2
##    word       score
##    <chr>      <int>
##  1 abandon       -2
##  2 abandoned     -2
##  3 abandons      -2
##  4 abducted      -2
##  5 abduction     -2
##  6 abductions    -2
##  7 abhor         -3
##  8 abhorred      -3
##  9 abhorrent     -3
## 10 abhors        -3
## # ... with 2,466 more rows

get_sentiments("bing")

## # A tibble: 6,788 x 2
##    word        sentiment
##    <chr>       <chr>    
##  1 2-faced     negative 
##  2 2-faces     negative 
##  3 a+          positive 
##  4 abnormal    negative 
##  5 abolish     negative 
##  6 abominable  negative 
##  7 abominably  negative 
##  8 abominate   negative 
##  9 abomination negative 
## 10 abort       negative 
## # ... with 6,778 more rows

get_sentiments("nrc")

## # A tibble: 13,901 x 2
##    word        sentiment
##    <chr>       <chr>    
##  1 abacus      trust    
##  2 abandon     fear     
##  3 abandon     negative 
##  4 abandon     sadness  
##  5 abandoned   anger    
##  6 abandoned   fear     
##  7 abandoned   negative 
##  8 abandoned   sadness  
##  9 abandonment anger    
## 10 abandonment fear     
## # ... with 13,891 more rows

Considerations

• They were constructed via either crowdsourcing (using, for example, Amazon Mechanical Turk) or by the labor of one of the authors, and were validated using some combination of crowdsourcing again, restaurant or movie reviews, or Twitter data.
• Thus, we need to consider to apply these sentiment lexicons to styles of text dramatically different from what they were validated on, such as narrative fiction from 200 years ago.
• Moreover, There are also some domain-specific sentiment lexicon available, constructed to be used with text from a specific content area.
• In conclusion, in this tutorial, it’s better to find the workflow of sentiment analysis and to get an insight of using this tutorial for your future context.

2. Sentiment Analysis with inner join

• Let’s look at the words with a joy score from the NRC lexicon. What are the most common joy words in Emma?

library(janeaustenr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(stringr)

tidy_books <- austen_books() %>%
  group_by(book) %>%
  mutate(linenumber = row_number(),
         chapter = cumsum(str_detect(text, regex("^chapter [\\divxlc]", 
                                                 ignore_case = TRUE)))) %>%
  ungroup() %>%
  unnest_tokens(word, text)
head(tidy_books)

## # A tibble: 6 x 4
##   book                linenumber chapter word       
##   <fct>                    <int>   <int> <chr>      
## 1 Sense & Sensibility          1       0 sense      
## 2 Sense & Sensibility          1       0 and        
## 3 Sense & Sensibility          1       0 sensibility
## 4 Sense & Sensibility          3       0 by         
## 5 Sense & Sensibility          3       0 jane       
## 6 Sense & Sensibility          3       0 austen

• Now that the text is in a tidy format with one word per row, we are ready to do the sentiment analysis. First, let’s use the NRC lexicon and filter() for the joy words. Next, let’s filter() the data frame with the text from the books for the words from Emma and then use inner_join() to perform the sentiment analysis. What are the most common joy words in Emma? Let’s use count() from dplyr.

nrc_joy <- get_sentiments("nrc") %>% 
  filter(sentiment == "joy")

tidy_books %>% 
  filter(book == "Emma") %>% 
  inner_join(nrc_joy) %>% 
  count(word, sort = TRUE)

## Joining, by = "word"

## # A tibble: 303 x 2
##    word        n
##    <chr>   <int>
##  1 good      359
##  2 young     192
##  3 friend    166
##  4 hope      143
##  5 happy     125
##  6 love      117
##  7 deal       92
##  8 found      92
##  9 present    89
## 10 kind       82
## # ... with 293 more rows

We see mostly positive, happy words about hope, friendship, and love here. Now, We can also examine how sentiment changes throughout each novel. + 1. we find a sentiment score for each word using the Bing lexicon and inner_join(). + 2. Next, we count up how many positive and negative words there are in defined sections of each book. We define an index here to keep track of where we are in the narrative; this index (using integer division) counts up sections of 80 lines of text.

The %/% operator does integer division (x %/% y is equivalent to floor(x/y)) so the index keeps track of which 80-line section of text we are counting up negative and positive sentiment in.

• 3. We then use spread() so that we have negative and positive sentiment in separate columns, and lastly calculate a net sentiment (positive - negative).

library(tidyr)

jane_austen_sentiment <- tidy_books %>% 
  inner_join(get_sentiments("bing")) %>% 
  count(book, index = linenumber %/% 80, sentiment) %>%
  spread(sentiment, n, fill = 0) %>% 
  mutate(sentiment = positive - negative)

## Joining, by = "word"

• Now we can plot these sentiment scores across the plot trajectory of each novel. Notice that we are plotting against the index on the x-axis that keeps track of narrative time in sections of text.

library(ggplot2)

ggplot(jane_austen_sentiment, aes(index, sentiment, fill = book)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~book, ncol = 2, scales = "free_x")

Comparing the three sentiment dictionaries

• Let’s do it more with other sentiment dictionaries and the narrative arc of Pride and Prejudice.

• First, let’s use filter() to choose only the words from the one novel we are interested in.

pride_prejudice <- tidy_books %>% 
  filter(book == "Pride & Prejudice")

head(pride_prejudice)

## # A tibble: 6 x 4
##   book              linenumber chapter word     
##   <fct>                  <int>   <int> <chr>    
## 1 Pride & Prejudice          1       0 pride    
## 2 Pride & Prejudice          1       0 and      
## 3 Pride & Prejudice          1       0 prejudice
## 4 Pride & Prejudice          3       0 by       
## 5 Pride & Prejudice          3       0 jane     
## 6 Pride & Prejudice          3       0 austen

• Second, Now, we can use inner_join() to calculate the sentiment in different ways.

Remember from above that the AFINN lexicon measures sentiment with a numeric score between -5 and 5, while the other two lexicons categorize words in a binary fashion, either positive or negative. To find a sentiment score in chunks of text throughout the novel, we will need to use a different pattern for the AFINN lexicon than for the other two.

• Third, dataset afinn

afinn <- pride_prejudice %>% 
  inner_join(get_sentiments("afinn")) %>% 
  group_by(index = linenumber %/% 80) %>% 
  summarise(sentiment = sum(score)) %>% 
  mutate(method = "AFINN")

## Joining, by = "word"

head(afinn)

## # A tibble: 6 x 3
##   index sentiment method
##   <dbl>     <int> <chr> 
## 1     0        29 AFINN 
## 2     1         0 AFINN 
## 3     2        20 AFINN 
## 4     3        30 AFINN 
## 5     4        62 AFINN 
## 6     5        66 AFINN

• Fourth, bing_and_nrc

bing_and_nrc <- bind_rows(pride_prejudice %>% 
                            inner_join(get_sentiments("bing")) %>% 
                            mutate(method = "Bing et al."), 
                          pride_prejudice %>% 
                            inner_join(get_sentiments("nrc") %>% 
                                         filter(sentiment %in% c("positive", "negative"))) %>% 
                            mutate(method = "NRC")
                          ) %>% 
  count(method, index = linenumber %/% 80, sentiment) %>% 
  spread(sentiment, n, fill = 0) %>% 
  mutate(sentiment = positive - negative)

## Joining, by = "word"
## Joining, by = "word"

head(bing_and_nrc)

## # A tibble: 6 x 5
##   method      index negative positive sentiment
##   <chr>       <dbl>    <dbl>    <dbl>     <dbl>
## 1 Bing et al.     0        7       21        14
## 2 Bing et al.     1       20       19        -1
## 3 Bing et al.     2       16       20         4
## 4 Bing et al.     3       19       31        12
## 5 Bing et al.     4       23       47        24
## 6 Bing et al.     5       15       49        34

• Now, we have an estimate of the net sentiment (positive - negative) in each chunk of the novel text for each sentiment lexicon. Let’s visualize all of them.

bind_rows(afinn, 
          bing_and_nrc) %>% 
  ggplot(aes(index, sentiment, fill = method)) + 
  geom_col(show.legend = FALSE) + 
  facet_wrap(~method, ncol = 1, scales = "free_y")

• Three plots are saying that the three different lexicons for calculating sentiment give results that are different in an absolute sense but have similar relative trajectories through the novel.
• Question, why is, the result for the NRC lexicon biased so high in sentiment compared to the Bing et al. result? Let’s look briefly at how many positive and negative words are in these lexicon.

get_sentiments("nrc") %>% 
     filter(sentiment %in% c("positive", 
                             "negative")) %>% 
  count(sentiment)

## # A tibble: 2 x 2
##   sentiment     n
##   <chr>     <int>
## 1 negative   3324
## 2 positive   2312

get_sentiments("bing") %>% 
  count(sentiment)

## # A tibble: 2 x 2
##   sentiment     n
##   <chr>     <int>
## 1 negative   4782
## 2 positive   2006

• Both lexicons have more negative than positive words, but the ratio of negative to positive words is higher in the Bing lexicon than the NRC lexicon. This will contribute to the effect we see in the plot above, as will any systematic difference in word matches, e.g. if the negative words in the NRC lexicon do not match the words that Jane Austen uses very well. Whatever the source of these differences, we see similar relative trajectories across the narrative arc, with similar changes in slope, but marked differences in absolute sentiment from lexicon to lexicon. This is all important context to keep in mind when choosing a sentiment lexicon for analysis.

• point 1. This comment makes me to ponder the direct usage of the given lexicons or packages.

• point 2. This comment makes me to motivate develop a specific lexcions for the each different context, using given lexicons

Most common positive and negative words

One advantage of having the data frame with both sentiment and word is that we can analyze word counts that contribute to each sentiment. By implementing count() here with arguments of both word and sentiment, we find out how much each word contributed to each sentiment.

bing_word_counts <- tidy_books %>% 
  inner_join(get_sentiments("bing")) %>% 
  count(word, sentiment, sort = TRUE) %>% 
  ungroup()

## Joining, by = "word"

head(bing_word_counts)

## # A tibble: 6 x 3
##   word   sentiment     n
##   <chr>  <chr>     <int>
## 1 miss   negative   1855
## 2 well   positive   1523
## 3 good   positive   1380
## 4 great  positive    981
## 5 like   positive    725
## 6 better positive    639

This can be shown visually, and we can pipe straight into ggplot2, if we like, because of the way we are consistently using tools built for handling tidy data frames.

bing_word_counts %>%
  group_by(sentiment) %>%
  top_n(10) %>%
  ungroup() %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(word, n, fill = sentiment)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~sentiment, scales = "free_y") +
  labs(y = "Contribution to sentiment",
       x = NULL) +
  coord_flip()

## Selecting by n

• Point 1. let us see the word “miss”. The word is coded as negative but it is often used unmarried women n Jane Austen’s works. So, if it were appropriate for our purposes, we could easily add “miss” to a custom stop-words list using bind_rows().

custom_stop_words <- bind_rows(data_frame(word = c("miss"), 
                                          lexicon = c("custom")), 
                               stop_words)

custom_stop_words

## # A tibble: 1,150 x 2
##    word        lexicon
##    <chr>       <chr>  
##  1 miss        custom 
##  2 a           SMART  
##  3 a's         SMART  
##  4 able        SMART  
##  5 about       SMART  
##  6 above       SMART  
##  7 according   SMART  
##  8 accordingly SMART  
##  9 across      SMART  
## 10 actually    SMART  
## # ... with 1,140 more rows

Wordclouds

library(wordcloud)

## Loading required package: RColorBrewer

tidy_books %>%
  anti_join(stop_words) %>%
  count(word) %>%
  with(wordcloud(word, n, max.words = 100)) 

## Joining, by = "word"

library(reshape2)

## 
## Attaching package: 'reshape2'

## The following object is masked from 'package:tidyr':
## 
##     smiths

tidy_books %>%
  inner_join(get_sentiments("bing")) %>%
  count(word, sentiment, sort = TRUE) %>%
  acast(word ~ sentiment, value.var = "n", fill = 0) %>%
  comparison.cloud(colors = c("gray20", "gray80"),
                   max.words = 100)

## Joining, by = "word"

Looking at units beyond just words

library(reshape2)

PandP_sentences <- data_frame(text = prideprejudice) %>% 
  unnest_tokens(sentence, text, token = "sentences")

PandP_sentences$sentence[2]

## [1] "however little known the feelings or views of such a man may be on his first entering a neighbourhood, this truth is so well fixed in the minds of the surrounding families, that he is considered the rightful property of some one or other of their daughters."

Bonus: Looking at units beyond just words

1. Another option in unnest_tokens() is to split into tokens using a regex pattern. We could use this, for example, to split the text of Jane Austen’s novels into a data frame by chapter.

austen_chapters <- austen_books() %>%
  group_by(book) %>%
  unnest_tokens(chapter, text, token = "regex", 
                pattern = "Chapter|CHAPTER [\\dIVXLC]") %>%
  ungroup()

austen_chapters %>% 
  group_by(book) %>% 
  summarise(chapters = n())

## # A tibble: 6 x 2
##   book                chapters
##   <fct>                  <int>
## 1 Sense & Sensibility       51
## 2 Pride & Prejudice         62
## 3 Mansfield Park            49
## 4 Emma                      56
## 5 Northanger Abbey          32
## 6 Persuasion                25

2. We can use tidy text analysis to ask questions such as what are the most negative chapters in each of Jane Austen’s novels?

• 1. First, let’s get the list of negative words from the Bing lexicon.
• 2. Second, let’s make a data frame of how many words are in each chapter so we can normalize for the length of chapters.
• 3. Then, let’s find the number of negative words in each chapter and divide by the total words in each chapter.
• 4. For each book, which chapter has the highest proportion of negative words?

bingnegative <- get_sentiments("bing") %>% 
  filter(sentiment == "negative")

wordcounts <- tidy_books %>%
  group_by(book, chapter) %>%
  summarize(words = n())

tidy_books %>%
  semi_join(bingnegative) %>%
  group_by(book, chapter) %>%
  summarize(negativewords = n()) %>%
  left_join(wordcounts, by = c("book", "chapter")) %>%
  mutate(ratio = negativewords/words) %>%
  filter(chapter != 0) %>%
  top_n(1) %>%
  ungroup()

## Joining, by = "word"

## Selecting by ratio

## # A tibble: 6 x 5
##   book                chapter negativewords words  ratio
##   <fct>                 <int>         <int> <int>  <dbl>
## 1 Sense & Sensibility      43           161  3405 0.0473
## 2 Pride & Prejudice        34           111  2104 0.0528
## 3 Mansfield Park           46           173  3685 0.0469
## 4 Emma                     15           151  3340 0.0452
## 5 Northanger Abbey         21           149  2982 0.0500
## 6 Persuasion                4            62  1807 0.0343

These are the chapters with the most sad words in each book, normalized for number of words in the chapter. What is happening in these chapters? + In Chapter 43 of Sense and Sensibility Marianne is seriously ill, near death. + In Chapter 34 of Pride and Prejudice Mr. Darcy proposes for the first time (so badly!). + In Chapter 46 of Mansfield Park is almost the end, when everyone learns of Henry’s scandalous adultery + In Chapter 15 of Emma is when horrifying Mr. Elton proposes, and in Chapter 21 of Northanger Abbey Catherine is deep in her Gothic faux fantasy of murder, etc. + In Chapter 4 of Persuasion is when the reader gets the full flashback of Anne refusing Captain Wentworth and how sad she was and what a terrible mistake she realized it to be.

The source codes and contens come from the book Text Mining with R Enjoy

1. The unnest_tokens function

text <- c("Because I could not stop for Death -",
          "He kindly stopped for me -",
          "The Carriage held but just Ourselves -",
          "and Immortality")

text

## [1] "Because I could not stop for Death -"  
## [2] "He kindly stopped for me -"            
## [3] "The Carriage held but just Ourselves -"
## [4] "and Immortality"

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

text_df <- data_frame(line = 1:4, text = text)

• What does it mean that this data frame has printed out as a “tibble”? A tibble is a modern class of data frame within R, available in the dplyr and tibble packages, that has a convenient print method, will not convert strings to factors, and does not use row names. Tibbles are great for use with tidy tools.

# install.packages("tidytext", dependencies = TRUE)
library(tidytext)

text_df %>% unnest_tokens(word, text)

## # A tibble: 20 x 2
##     line word       
##    <int> <chr>      
##  1     1 because    
##  2     1 i          
##  3     1 could      
##  4     1 not        
##  5     1 stop       
##  6     1 for        
##  7     1 death      
##  8     2 he         
##  9     2 kindly     
## 10     2 stopped    
## 11     2 for        
## 12     2 me         
## 13     3 the        
## 14     3 carriage   
## 15     3 held       
## 16     3 but        
## 17     3 just       
## 18     3 ourselves  
## 19     4 and        
## 20     4 immortality

• The two basic arguments to unnest_tokens used here are column names. First we have the output column name that will be created as the text is unnested into it (word, in this case), and then the input column that the text comes from (text, in this case). Remember that text_df above has a column called text that contains the data of interest.

• Having the text data in this format lets us manipulate, process, and visualize the text using the standard set of tidy tools, namely dplyr, tidyr, and ggplot2, as shown in Figure 1.1 Figure_A_FlowChart.png

knitr::include_graphics("img/Figure_A_FlowChart.png")

2. Tidying the works of Jane Austen

# install.packages("janeaustenr")
library(janeaustenr)
library(dplyr)
library(stringr)

original_books <- austen_books() %>% 
    group_by(book) %>% 
    mutate(linenumber = row_number(), 
           chapter = cumsum(str_detect(text, 
                            regex("^chapter [\\divxlc]",                       ignore_case = TRUE)))) %>% 
    ungroup()

original_books %>% head()

## # A tibble: 6 x 4
##   text                  book                linenumber chapter
##   <chr>                 <fct>                    <int>   <int>
## 1 SENSE AND SENSIBILITY Sense & Sensibility          1       0
## 2 ""                    Sense & Sensibility          2       0
## 3 by Jane Austen        Sense & Sensibility          3       0
## 4 ""                    Sense & Sensibility          4       0
## 5 (1811)                Sense & Sensibility          5       0
## 6 ""                    Sense & Sensibility          6       0

To work with this as a tidy dataset, we need to restructure it in the one-token-per-row format, which as we saw earlier is done with the unnest_tokens() function.

# install.packages("janeaustenr")
library(tidytext)
tidy_books <- original_books %>% 
    unnest_tokens(word, text)

tidy_books %>% head()

## # A tibble: 6 x 4
##   book                linenumber chapter word       
##   <fct>                    <int>   <int> <chr>      
## 1 Sense & Sensibility          1       0 sense      
## 2 Sense & Sensibility          1       0 and        
## 3 Sense & Sensibility          1       0 sensibility
## 4 Sense & Sensibility          3       0 by         
## 5 Sense & Sensibility          3       0 jane       
## 6 Sense & Sensibility          3       0 austen

• Often in text analysis, we will want to remove stop words; stop words are words that are not useful for an analysis, typically extremely common words such as “the”, “of”, “to”, and so forth in English. We can remove stop words (kept in the tidytext dataset stop_words) with an anti_join()

data(stop_words)

tidy_books <- tidy_books %>%
  anti_join(stop_words)

## Joining, by = "word"

We can also use dplyr’s count() to find the most common words in all the books as a whole.

tidy_books %>%
  count(word, sort = TRUE)

## # A tibble: 13,914 x 2
##    word       n
##    <chr>  <int>
##  1 miss    1855
##  2 time    1337
##  3 fanny    862
##  4 dear     822
##  5 lady     817
##  6 sir      806
##  7 day      797
##  8 emma     787
##  9 sister   727
## 10 house    699
## # ... with 13,904 more rows

library(ggplot2)

tidy_books %>%
  count(word, sort = TRUE) %>%
  filter(n > 600) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(word, n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() + 
  labs(caption = "Figure 1.2 TOP words in Jane Austen’s novels")

3. Word Frequencies

• 35 - The Time Machine
• 36 - The War of the Worlds
• 5230 - The Invisible Man
• 159 - The Island of Doctor Moreau

library(gutenbergr)
hgwells <- gutenberg_download(c(35, 36, 5230, 159))

## Determining mirror for Project Gutenberg from http://www.gutenberg.org/robot/harvest

## Using mirror http://aleph.gutenberg.org

library(tidyverse)

## ── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──

## ✔ tibble  1.4.2     ✔ readr   1.3.0
## ✔ tidyr   0.8.2     ✔ purrr   0.2.5
## ✔ tibble  1.4.2     ✔ forcats 0.3.0

## ── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(tidytext)

tidy_hgwells <- hgwells %>%
  unnest_tokens(word, text) %>%
  anti_join(stop_words)

## Joining, by = "word"

tidy_hgwells %>% 
    count(word, sort = TRUE)

## # A tibble: 11,769 x 2
##    word       n
##    <chr>  <int>
##  1 time     454
##  2 people   302
##  3 door     260
##  4 heard    249
##  5 black    232
##  6 stood    229
##  7 white    222
##  8 hand     218
##  9 kemp     213
## 10 eyes     210
## # ... with 11,759 more rows

Now let’s get some well-known works of the Brontë sisters, whose lives overlapped with Jane Austen’s somewhat but who wrote in a rather different style. Let’s get Jane Eyre, Wuthering Heights, The Tenant of Wildfell Hall, Villette, and Agnes Grey.

library(gutenbergr)

bronte <- gutenberg_download(c(1260, 768, 969, 9182, 767))

library(tidyverse)
library(tidytext)

tidy_bronte <- bronte %>%
  unnest_tokens(word, text) %>%
  anti_join(stop_words)

## Joining, by = "word"

tidy_bronte %>%
  count(word, sort = TRUE)

## # A tibble: 23,050 x 2
##    word       n
##    <chr>  <int>
##  1 time    1065
##  2 miss     855
##  3 day      827
##  4 hand     768
##  5 eyes     713
##  6 night    647
##  7 heart    638
##  8 looked   601
##  9 door     592
## 10 half     586
## # ... with 23,040 more rows

• Interesting that “time”, “eyes”, and “hand” are in the top 10 for both H.G. Wells and the Brontë sisters.

Let’s calculate the frequency for each word for the works of Jane Austen, the Brontë sisters, and H.G. Wells by binding the data frames together. We can use spread and gather from tidyr to reshape our dataframe so that it is just what we need for plotting and comparing the three sets of novels.

library(tidyr)

frequency <- bind_rows(mutate(tidy_bronte, author = "Brontë Sisters"),
                       mutate(tidy_hgwells, author = "H.G. Wells"), 
                       mutate(tidy_books, author = "Jane Austen")) %>% 
  mutate(word = str_extract(word, "[a-z']+")) %>%
  count(author, word) %>%
  group_by(author) %>%
  mutate(proportion = n / sum(n)) %>% 
  select(-n) %>% 
  spread(author, proportion) %>% 
  gather(author, proportion, `Brontë Sisters`:`H.G. Wells`)

We use str_extract() here because the UTF-8 encoded texts from Project Gutenberg have some examples of words with underscores around them to indicate emphasis (like italics). The tokenizer treated these as words, but we don’t want to count “any” separately from “any” as we saw in our initial data exploration before choosing to use str_extract().

4. Visualization

Words that are close to the line in these plots have similar frequencies in both sets of texts, for example, in both Austen and Brontë texts (“miss”, “time”, “day” at the upper frequency end) or in both Austen and Wells texts (“time”, “day”, “brother” at the high frequency end).

library(scales)

## 
## Attaching package: 'scales'

## The following object is masked from 'package:purrr':
## 
##     discard

## The following object is masked from 'package:readr':
## 
##     col_factor

library(ggplot2)

# expect a warning about rows with missing values being removed
ggplot(frequency, aes(x = proportion, y = `Jane Austen`, color = abs(`Jane Austen` - proportion))) +
  geom_abline(color = "gray40", lty = 2) +
  geom_jitter(alpha = 0.1, size = 2.5, width = 0.3, height = 0.3) +
  geom_text(aes(label = word), check_overlap = TRUE, vjust = 1.5) +
  scale_x_log10(labels = percent_format()) +
  scale_y_log10(labels = percent_format()) +
  scale_color_gradient(limits = c(0, 0.001), low = "darkslategray4", high = "gray75") +
  facet_wrap(~author, ncol = 2) +
  theme(legend.position="none") +
  labs(y = "Jane Austen", 
       x = NULL, 
       caption = "Figure 1.3 Comparing the word frequencies of Jane Austen, the Brontë sisters, and H.G. Wells")

## Warning: Removed 41357 rows containing missing values (geom_point).

## Warning: Removed 41359 rows containing missing values (geom_text).

5. Correlation

Let’s quantify how similar and different these sets of word frequencies are using a correlation test. How correlated are the word frequencies between Austen and the Brontë sisters, and between Austen and Wells?

cor.test(data = frequency[frequency$author == "Brontë Sisters",],
         ~ proportion + `Jane Austen`)

## 
##  Pearson's product-moment correlation
## 
## data:  proportion and Jane Austen
## t = 119.65, df = 10404, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7527869 0.7689641
## sample estimates:
##       cor 
## 0.7609938

cor.test(data = frequency[frequency$author == "H.G. Wells",], 
         ~ proportion + `Jane Austen`)

## 
##  Pearson's product-moment correlation
## 
## data:  proportion and Jane Austen
## t = 36.441, df = 6053, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4032800 0.4445987
## sample estimates:
##       cor 
## 0.4241601

Just as we saw in the plots, the word frequencies are more correlated between the Austen and Brontë novels than between Austen and H.G. Wells.

What is Applying Statistical Models?

First, it is to make predictions about an outcome. Second, it is to run experiments to study relationship between variables. Third, it is to explore data to identify relationships among variables.

Basic Choices in model architecture

If Categorical response variable (e.g. yes or no, infected or not), then use rpart(). If Numerical response variable (e.g. unemployment rate), then use lm() or rpart(), but in this case, if the case is relevant with graual or proportional, then use lm. If the case is related with Dichotomous or discontinous, then use rpart().

The Prediction Errors?

When you train and test a model, you use data with values for the explanatory variables as well as the response variable.

If the model is good, when provided with the inputs from the testing data, the outputs from the function will give results “close” to the response variable in the testing data. Here is one question. How to measure “close”?

The first step is to subtract the function output from the actual response values in the testing data. The result is called the prediction error and there will be one such error for every case in the testing data. You then summarize that set of prediction errors.

The way to summarise the prediction erros is to calculate the mean of the square of the prediction errors. Since the errors are squared, none of them are negative. It means that the sum of errors reflects the magnitude and not the sign of the errors.

Step 1. Build Model

library(tidyverse)

## ── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 3.1.0     ✔ purrr   0.2.5
## ✔ tibble  1.4.2     ✔ dplyr   0.7.8
## ✔ tidyr   0.8.2     ✔ stringr 1.3.1
## ✔ readr   1.3.0     ✔ forcats 0.3.0

## ── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

# Data Import 
Runners_100 <- read_csv("https://assets.datacamp.com/production/course_1585/datasets/Runners100.csv") %>% 
    select(-orig.id) %>% 
    sample_n(100)

## Parsed with column specification:
## cols(
##   age = col_double(),
##   net = col_double(),
##   gun = col_double(),
##   sex = col_character(),
##   year = col_double(),
##   previous = col_double(),
##   nruns = col_double(),
##   start_position = col_character(),
##   orig.id = col_double()
## )

# Data Overview
glimpse(Runners_100)

## Observations: 100
## Variables: 8
## $ age            <dbl> 45, 36, 33, 28, 33, 53, 30, 32, 27, 31, 24, 33,...
## $ net            <dbl> 88.52, 101.20, 113.10, 69.70, 99.67, 76.88, 81....
## $ gun            <dbl> 89.20, 103.40, 117.80, 70.17, 104.50, 77.85, 82...
## $ sex            <chr> "M", "F", "M", "M", "F", "M", "M", "F", "F", "F...
## $ year           <dbl> 2000, 2000, 2002, 2006, 2003, 2006, 2005, 2004,...
## $ previous       <dbl> 1, 1, 1, 2, 0, 0, 4, 2, 2, 1, 1, 2, 3, 1, 2, 1,...
## $ nruns          <dbl> 4, 9, 3, 4, 3, 3, 6, 3, 4, 3, 3, 4, 4, 7, 5, 3,...
## $ start_position <chr> "eager", "calm", "mellow", "eager", "mellow", "...

# Build a model of net running time
base_model <- lm(net ~ age + sex, data = Runners_100)

# Evaluate base_model on the training data
base_model_output <- predict(base_model, newdata = Runners_100)

# Build the augmented model
aug_model <- lm(net ~ age + sex + previous, data = Runners_100)

# Evaluate aug_model on the training data
aug_model_output <- predict(aug_model, newdata = Runners_100)

# How much do the model outputs differ?
mean((base_model_output - aug_model_output) ^ 2, na.rm = TRUE)

## [1] 0.5157921

As you can see the result, adding previous as an explanatory variable changes the model outputs.

Step 2. Prediction Performace

One point. Knowing that the models make different predictions doesn’t tell you which model is better. In this exercise, you’ll compare the models’ predictions to the actual values of the response variable. The term prediction error or just error is often used, rather than difference. So, rather than speaking of the mean square difference, we’ll say mean square error.

# Build and evaluate the base model on Runners_100
base_model <- lm(net ~ age + sex, data = Runners_100)
base_model_output <- predict(base_model, newdata = Runners_100)

# Build and evaluate the augmented model on Runners_100
aug_model <- lm(net ~ age + sex + previous, data = Runners_100)
aug_model_output <- predict(aug_model, newdata = Runners_100)

# Find the case-by-case differences
base_model_differences <- with(Runners_100, net - base_model_output)
aug_model_differences <- with(Runners_100, net - aug_model_output)

# Calculate mean square errors
mean(base_model_differences ^ 2)

## [1] 131.5594

mean(aug_model_differences ^ 2)

## [1] 131.0436

The augmented model gives slightly better predictions. But, is it correct to compare two models?

Step 3. The importance of Statistics

data(CPS85, package = "mosaicData")

# Add bogus column to CPS85
CPS85$bogus <- rnorm(nrow(CPS85)) > 0

# Make the base model
base_model <- lm(wage ~ educ + sector + sex, data = CPS85)

# Make the bogus augmented model
aug_model <- lm(wage ~ educ + sector + sex + bogus, data = CPS85)

# Find the MSE of the base model
mean((CPS85$wage - predict(base_model, newdata = CPS85)) ^ 2)

## [1] 19.73308

# Find the MSE of the augmented model
mean((CPS85$wage - predict(aug_model, newdata = CPS85)) ^ 2)

## [1] 19.68466

Surprisingly, although you just add no genuine explanatory variable like bogus to model, MSE is smaller in the expanded model than in the base model. This is called The Null Model. This results in usage of cross validation.

Step 4. Testing and training datasets

The code in the editor uses a style that will give you two prediction error results: one for the training cases and one for the testing cases. Your goal is to see whether there is a systematic difference between prediction accuracy on the training and on the testing cases.

Since the split is being done at random, the results will vary somewhat each time you do the calculation.

# Generate a random TRUE or FALSE for each case in Runners_100
Runners_100$training_cases <- rnorm(nrow(Runners_100)) > 0

# Build base model net ~ age + sex with training cases
base_model <- lm(net ~ age + sex, data = subset(Runners_100, training_cases))

# Evaluate the model for the testing cases
# Install devtools if necessary
# install.packages("devtools")
# Install statisticalModeling
devtools::install_github("dtkaplan/statisticalModeling")

## Skipping install of 'statisticalModeling' from a github remote, the SHA1 (4c5383d3) has not changed since last install.
##   Use `force = TRUE` to force installation

library(statisticalModeling)
Preds <- evaluate_model(base_model, data = subset(Runners_100, !training_cases))

# Calculate the MSE on the testing data
with(data = Preds, mean((net - model_output)^2))

## [1] 136.3962

Step 5. Repeating Random Trials

• To simplifty things, the cv_pred_error() function in the statisticalModeling package will carry out this repetitive process for you. The function will do all the work of creating training and testing sets for each trial and calculating the mean square error for each trial.
• The context for this exercise is to see whether the prediction error calculated from the training data is consistently different from the cross-validated prediction error. To that end, you’ll calculate the in-sample error using only the training data. Then, you’ll do the cross validation and use a t-test to see if the in-sample error is statistically different from the cross-validated error.

# The model
model <- lm(net ~ age + sex, data = Runners_100)

# Find the in-sample error (using the training data)
in_sample <- evaluate_model(model, data = Runners_100)
in_sample_error <- 
  with(in_sample, mean((net - model_output)^2, na.rm = TRUE))

# Calculate MSE for many different trials
trials <- cv_pred_error(model)

# View the cross-validated prediction errors
trials

##        mse model
## 1 137.5116 model
## 2 145.9868 model
## 3 142.5793 model
## 4 142.1681 model
## 5 139.8571 model

# Find confidence interval on trials and compare to training_error
mosaic::t.test(~ mse, mu = in_sample_error, data = trials)

## 
##  One Sample t-test
## 
## data:  mse
## t = 7.0898, df = 4, p-value = 0.00209
## alternative hypothesis: true mean is not equal to 131.5594
## 95 percent confidence interval:
##  137.6805 145.5606
## sample estimates:
## mean of x 
##  141.6206

The error based on the training data is below the 95% confidence interval representing the cross-validated prediction error.

Step 6. To add or not to add (an explanatory variable)?

you’re going to use cross validation to find out whether adding a new explanatory variable improves the prediction performance of a model. Remember that models are biased to perform well on the training data. Cross validation gives a fair indication of the prediction error on new data.

# The base model
base_model <- lm(net ~ age + sex, data = Runners_100)

# An augmented model adding previous as an explanatory variable
aug_model <- lm(net ~ age + sex + previous, data = Runners_100)

# Run cross validation trials on the two models
trials <- cv_pred_error(base_model, aug_model)
trials

##         mse      model
## 1  142.0409 base_model
## 2  136.6856 base_model
## 3  143.4253 base_model
## 4  137.9605 base_model
## 5  134.6230 base_model
## 6  143.2968  aug_model
## 7  142.2867  aug_model
## 8  143.6135  aug_model
## 9  140.3348  aug_model
## 10 145.8624  aug_model

# Compare the two sets of cross-validated errors
t.test(mse ~ model, data = trials)

## 
##  Welch Two Sample t-test
## 
## data:  mse by model
## t = 2.1983, df = 6.1923, p-value = 0.06887
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.4327813  8.6963517
## sample estimates:
##  mean in group aug_model mean in group base_model 
##                 143.0788                 138.9471

• Notice that cross validation reveals that the augmented model makes worse predictions (larger prediction error) than the base model. Bigger is not necessarily better when it comes to modeling!

• In Conclusion, it’s not good to add the previous variable, just use base_model in this case.