This is a bit of a diversion for a public-health-focused course in data science, but it’s fun, related to web data, strings, and factors, and emphasizes tools in data wrangling. It’s most closely related to content in the Data Wrangling II topic.

## Overview

### Learning Objectives

Use tidytext to organize text data, and to conduct frequency and sentiment analyses.

## Example

I’ll write code for today’s content in a new R Markdown document called tidy_text.Rmd, and put it in the extra topics directory / GitHub repo. I’m going to load the tidyverse as usual, as well as tidytext and rvest.

library(tidyverse)
library(tidytext)

library(rvest)

### Data

We’re sticking with “Napoleon Dynamite reviews”! First, I’ll re-use code from iteration and listcols to scrape the 1000 most recent reviews on Amazon (and cache the result).

read_page_reviews <- function(url) {

review_titles =
html %>%
html_nodes(".a-text-bold span") %>%
html_text()

review_stars =
html %>%
html_nodes("#cm_cr-review_list .review-rating") %>%
html_text() %>%
str_extract("^\\d") %>%
as.numeric()

review_text =
html %>%
html_nodes(".review-text-content span") %>%
html_text() %>%
str_replace_all("\n", "") %>%
str_trim()

tibble(
title = review_titles,
stars = review_stars,
text = review_text
)
}

url_base = "https://www.amazon.com/product-reviews/B00005JNBQ/ref=cm_cr_arp_d_viewopt_rvwer?ie=UTF8&reviewerType=avp_only_reviews&sortBy=recent&pageNumber="

dynamite_reviews =
tibble(
page = 3:100,
urls = str_c(url_base, page)) %>%
mutate(reviews = map(urls, read_page_reviews)) %>%
unnest(reviews) %>%
mutate(review_num = row_number()) %>%
relocate(page, review_num)

The output of the code above is a successfully scraped dataset with 6 and 980 rows – one row for each review. For each review we get the title of that review, the number of stars it received, and text that describers the users feelings about the movie.

### Words and wordcounts

To illustrate tidy text and text analysis, we’ll focus on the reviews directly, which are stored as strings in text. To begin our analysis, we’ll un-nest the tokens (i.e. words) in each row; the result is a tidy dataset in which each word is contained within a separate row.

dynamite_words =
dynamite_reviews %>%
unnest_tokens(word, text)

There are lots of words here that are uninformative. We’ll remove “stop words” using anti_join; in other settings the words you want to remove might be different.

data(stop_words)

dynamite_words =
anti_join(dynamite_words, stop_words)
## Joining, by = "word"

Great! Let’s take a look at the most commonly used (informative) words in this dataset.

dynamite_words %>%
count(word, sort = TRUE) %>%
top_n(10) %>%
mutate(word = fct_reorder(word, n)) %>%
ggplot(aes(x = word, y = n)) +
geom_bar(stat = "identity", fill = "blue", alpha = .6) +
coord_flip()
## Selecting by n

### Comparing words across groups

The next code chunk below produces a table of the most frequently used in one- and five-star reviews.

dynamite_words %>%
filter(stars %in% c(1, 5)) %>%
group_by(stars) %>%
count(word) %>%
top_n(5) %>%
knitr::kable()
## Selecting by n
stars word n
1 boring 9
1 film 10
1 funny 10
1 movie 53
1 time 10
1 watch 9
5 classic 98
5 funny 117
5 love 120
5 movie 439
5 time 99

The table above gives the top 5 most frequently used words in 1-star and 5-star reviews. Movie is the most used word for both 1 and 5-star reviews, though other words, like dumb differentiate 1-star reviews from 5-star reviews, which have words like love.

Word frequency might be misleading because there are 795 5-star reviews and only 76 1-star reviews.

Let’s compare which words are more likely to come from a 1 versus 5 star ratings. We limit to words that appear at least 5 times and compute the approximate log odds ratio for each word.

word_ratios =
dynamite_words %>%
filter(stars %in% c(1, 5)) %>%
count(word, stars) %>%
group_by(word) %>%
filter(sum(n) >= 5) %>%
ungroup() %>%
pivot_wider(
names_from = stars,
values_from = n,
names_prefix = "stars_",
values_fill = 0) %>%
mutate(
stars_1_odds = (stars_1 + 1) / (sum(stars_1) + 1),
stars_5_odds = (stars_5 + 1) / (sum(stars_5) + 1),
log_OR = log(stars_5_odds / stars_1_odds)
) %>%
arrange(desc(log_OR)) 

Next, let’s plot the top 10 most distinctive words (that is, words that appear much more frequently in one group than the other) below.

word_ratios %>%
mutate(pos_log_OR = ifelse(log_OR > 0, "5 star > 1 star", "1 star > 5 star")) %>%
group_by(pos_log_OR) %>%
top_n(10, abs(log_OR)) %>%
ungroup() %>%
mutate(word = fct_reorder(word, log_OR)) %>%
ggplot(aes(word, log_OR, fill = pos_log_OR)) +
geom_col() +
coord_flip() +
ylab("log odds ratio (5/1)") +
scale_fill_discrete(name = "")

Words like “classic”, “awesome”, and “love” have high relative frequency in the 5-star reviews and “boring”, “dumb”, and “bad” have high relative frequency in the 1-star reviews. This seems to be a polarizing film.

### Sentiment analysis

Finally, let’s score the sentiment in each word. We’ll use the “bing” (like Bing Liu, not like bing.com) sentiment lexicon, which simply categorizes each word as having a positive or negative sentiment.

bing_sentiments = get_sentiments("bing")

Note this is might not always be appropriate – this scores cold as negative which might not be accurate for e.g. food inspections – but we’ll use it anyway.

We need to combine this lexicon with our tidy dataset containing words from each inspection. Note that only words that are in the sentiment lexicon will be retained, as the rest of the words are not considered meaningful. We’ll also count the number of positive and negative words in each review, and create a score that is the difference between the number of positive words and negative words.

dynamite_sentiments =
dynamite_words %>%
inner_join(., bing_sentiments) %>%
count(review_num, sentiment) %>%
pivot_wider(
names_from = sentiment,
values_from = n,
values_fill = 0) %>%
mutate(review_sentiment = positive - negative) %>%
select(review_num, review_sentiment)
## Joining, by = "word"

We now have sentiment scores for each inspection. We’ll combine these with our original dataset, which had inspections in each row rather than words in each row – the data tidied for text analysis aren’t really suitable for our current needs.

dynamite_sentiments =
right_join(
dynamite_reviews, dynamite_sentiments,
by = "review_num")

Finally, let’s make a plot showing inspection sentiments and stars.

dynamite_sentiments %>%
mutate(
review_num = factor(review_num),
review_num = fct_reorder(review_num, review_sentiment, .desc = TRUE)) %>%
ggplot(aes(x = review_num, y = review_sentiment, fill = stars, color = stars)) +
geom_bar(stat = "identity") +
theme(
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())

Sentiment seems to be at least somewhat associated with star rating in that more positive sentiments are more yellow-green (4-5 stars) and more negative sentiments are more blue-purple.

Here is the text from the most positive review:

dynamite_sentiments %>%
filter(review_sentiment == max(review_sentiment)) %>%
pull(text)
## [1] "love love love love love. We watch this twice a week and even had a Napoleon Dynamite party complete with corn dogs and tater tots. FUN!!"

And here is the text from the most negative (1-star) review:

dynamite_sentiments %>%
filter(review_sentiment == min(review_sentiment), stars == 1) %>%
pull(text)
## character(0)

## Other materials

• The framework we used is explained in detail in the Tidy Text book
• One of the book’s authors, Julia Silge, has a nice video talking about the work
• The other of the book’s authors, Dave Robinson, used the approach to examine Donald Trump’s tweets in this this blog post

The code that I produced working examples in lecture is here.