Tag: textanalytics

Summarizing our Daily News: Clustering 100.000+ Articles in Python

Summarizing our Daily News: Clustering 100.000+ Articles in Python

Andrew Thompson was interested in what 10 topics a computer would identify in our daily news. He gathered over 140.000 new articles from the archives of 10 different sources, as you can see in the figure below.

The sources of the news articles used in the analysis.

In Python, Andrew converted the text of all these articles into a manageable form (tf-idf document term matrix (see also Harry Plotter: Part 2)), reduced these data to 100 dimensions using latent semantic analysis (singular value decomposition), and ran a k-means clustering to retrieve the 10 main clusters. I included his main results below, but I highly suggest you visit the original article on Medium as Andrew used Plotly to generate interactive plots!

newplot
Most important words per topic (interactive visual in original article)

The topics structure seems quite nice! Topic 0 involves legal issues, such as immigration, whereas topic 1 seems to be more about politics. Topic 8 is clearly sports whereas 9 is education. Next, Andres inspected which media outlet covers which topics most. Again, visit the original article for interactive plots!

newplot (1).png
Media outlets and the topics they cover (interactive version in original article)

In light of the fake news crisis and the developments in (internet) media, I believe Andrew’s conclusions on these data are quite interesting.

I suppose different people could interpret this data and these graphs differently, but I interpret them as the following: when forced into groups, the publications sort into Reuters and everything else.

[…]

Every publication in this dataset except Reuters shares some common denominators. They’re entirely funded on ads and/or subscriptions (Vox and BuzzFeed also have VC funding, but they’re ad-based models), and their existence relies on clicks. By contrast, Reuters’s news product is merely the public face of a massive information conglomerate. Perhaps more importantly, it’s a news wire whose coverage includes deep reporting on the affairs of our financial universe, and therefore is charged with a different mandate than the others — arguably more than the New York Times, it must cover all the news, without getting trapped in the character driven reality-TV spectacle that every other citizen of the dataset appears to so heavily relish in doing. Of them all, its voice tends to maintain the most moderate indoor volume, and no single global event provokes larger-than-life outrage, if outrage can be provoked from Reuters at all. Perhaps this is the product of belonging to the financial press and analyzing the world macroscopically; the narrative of the non-financial press fails to accord equal weight to a change in the LIBOR rate and to the policy proposals of a madman, even though it arguably should. Every other publication here seems to bear intimations of utopia, and the subtext of their content is often that a perfect world would materialize if we mixed the right ingredients in the recipe book, and that the thing you’re outraged about is actually the thing standing between us and paradise. In my experience as a reader, I’ve never felt anything of the sort emanate from Reuters.

This should not be interpreted as asserting that the New York Times and Breitbart are therefore identical cauldrons of apoplexy. I read a beautifully designed piece today in the Times about just how common bioluminescence is among deep sea creatures. It goes without saying that the prospect of finding a piece like that in Breitbart is nonexistent, which is one of the things I find so god damned sad about that territory of the political spectrum, as well as in its diametrical opponents a la Talking Points Memo. But this is the whole point: show an algorithm the number of stories you write about deep sea creatures and it’ll show you who you are. At a finer resolution, we would probably find a chasm between the Times and Fox News, or between NPR and the New York Post. See that third cluster up there, where all the words are kind of compressed with lower TfIdf values and nothing sticks out? It’s actually a whole jungle of other topics, and you can run the algorithm on just that cluster and get new groups and distinctions — and one of those clusters will also be a compression of different kinds of stories, and you can do this over and over in a fractal of machine learning. The distinction here is not the only one, but it is, from the aerial perspective of data, the first.

It would be really interesting to see whether more high-quality media outlets, like the New York Times, could be easily distinguished from more sensational outlets, such as Buzzfeed, when more clusters were used, or potentially other text analytics methodology, like latent Dirichlet allocation.

Harry Plotter: Part 2 – Hogwarts Houses and their Stereotypes

Harry Plotter: Part 2 – Hogwarts Houses and their Stereotypes

Two weeks ago, I started the Harry Plotter project to celebrate the 20th anniversary of the first Harry Potter book. I could not have imagined that the first blog would be so well received. It reached over 4000 views in a matter of days thanks to the lovely people in the data science and #rstats community that were kind enough to share it (special thanks to MaraAverick and DataCamp). The response from the Harry Potter community, for instance on reddit, was also just overwhelming

Part 2: Hogwarts Houses

All in all, I could not resist a sequel and in this second post we will explore the four houses of Hogwarts: GryffindorHufflepuffRavenclaw, and Slytherin. At the end of today’s post we will end up with visualizations like this:

treemap_house_word_ratio

Various stereotypes exist regarding these houses and a textual analysis seemed a perfect way to uncover their origins. More specifically, we will try to identify which words are most unique, informative, important or otherwise characteristic for each house by means of ratio and tf-idf statistics. Additionally, we will try to estime a personality profile for each house using these characteristic words and the emotions they relate to. Again, we rely strongly on ggplot2 for our visualizations, but we will also be using the treemaps of treemapify. Moreover, I have a special surprise this second post, as I found the orginal Harry Potter font, which will definately make the visualizations feel more authentic. Of course, we will conduct all analyses in a tidy manner using tidytext and the tidyverse.

I hope you will enjoy this blog and that you’ll be back for more. To be the first to receive new content, please subscribe to my website www.paulvanderlaken.com, follow me on Twitter, or add me on LinkedIn. Additionally, if you would like to contribute to, collaborate on, or need assistance with a data science project or venture, please feel free to reach out.

R Setup

All analysis were performed in RStudio, and knit using rmarkdown so that you can follow my steps.

In term of setup, we will be needing some of the same packages as last time. Bradley Boehmke gathered the text of the Harry Potter books in his harrypotter package. We need devtools to install that package the first time, but from then on can load it in as usual. We need plyr for ldply(). We load in most other tidyverse packages in a single bundle and add tidytext. Finally, I load the Harry Potter font and set some default plotting options.

# SETUP ####
# LOAD IN PACKAGES
# library(devtools)
# devtools::install_github("bradleyboehmke/harrypotter")
library(harrypotter)
library(plyr)
library(tidyverse)
library(tidytext)

# VIZUALIZATION SETTINGS
# custom Harry Potter font
# http://www.fontspace.com/category/harry%20potter
library(extrafont)
font_import(paste0(getwd(),"/fontomen_harry-potter"), prompt = F) # load in custom Harry Potter font
windowsFonts(HP = windowsFont("Harry Potter"))
theme_set(theme_light(base_family = "HP")) # set default ggplot theme to light
default_title = "Harry Plotter: References to the Hogwarts houses" # set default title
default_caption = "www.paulvanderlaken.com" # set default caption
dpi = 600 # set default dpi

Importing and Transforming Data

Before we import and transform the data in one large piping chunk, I need to specify some variables.

First, I tell R the house names, which we are likely to need often, so standardization will help prevent errors. Next, my girlfriend was kind enough to help me (colorblind) select the primary and secondary colors for the four houses. Here, the ggplot2 color guide in my R resources list helped a lot! Finally, I specify the regular expression (tutorials) which we will use a couple of times in order to identify whether text includes either of the four house names.

# DATA PREPARATION ####
houses <- c('gryffindor', 'ravenclaw', 'hufflepuff', 'slytherin') # define house names
houses_colors1 <- c("red3", "yellow2", "blue4", "#006400") # specify primary colors
houses_colors2 <- c("#FFD700", "black", "#B87333", "#BCC6CC") # specify secondary colors
regex_houses <- paste(houses, collapse = "|") # regular expression

Import Data and Tidy

Ok, let’s import the data now. You may recognize pieces of the code below from last time, but this version runs slightly smoother after some optimalization. Have a look at the current data format.

# LOAD IN BOOK TEXT 
houses_sentences <- list(
  `Philosophers Stone` = philosophers_stone,
  `Chamber of Secrets` = chamber_of_secrets,
  `Prisoner of Azkaban` = prisoner_of_azkaban,
  `Goblet of Fire` = goblet_of_fire,
  `Order of the Phoenix` = order_of_the_phoenix,
  `Half Blood Prince` = half_blood_prince,
  `Deathly Hallows` = deathly_hallows
) %>% 
  # TRANSFORM TO TOKENIZED DATASET
  ldply(cbind) %>% # bind all chapters to dataframe
  mutate(.id = factor(.id, levels = unique(.id), ordered = T)) %>% # identify associated book
  unnest_tokens(sentence, `1`, token = 'sentences') %>% # seperate sentences
  filter(grepl(regex_houses, sentence)) %>% # exclude sentences without house reference
  cbind(sapply(houses, function(x) grepl(x, .$sentence)))# identify references
# examine
max.char = 30 # define max sentence length
houses_sentences %>%
  mutate(sentence = ifelse(nchar(sentence) > max.char, # cut off long sentences
                           paste0(substring(sentence, 1, max.char), "..."),
                           sentence)) %>% 
  head(5)
##                  .id                          sentence gryffindor
## 1 Philosophers Stone "well, no one really knows unt...      FALSE
## 2 Philosophers Stone "and what are slytherin and hu...      FALSE
## 3 Philosophers Stone everyone says hufflepuff are a...      FALSE
## 4 Philosophers Stone "better hufflepuff than slythe...      FALSE
## 5 Philosophers Stone "there's not a single witch or...      FALSE
##   ravenclaw hufflepuff slytherin
## 1     FALSE       TRUE      TRUE
## 2     FALSE       TRUE      TRUE
## 3     FALSE       TRUE     FALSE
## 4     FALSE       TRUE      TRUE
## 5     FALSE      FALSE      TRUE

Transform to Long Format

Ok, looking great, but not tidy yet. We need gather the columns and put them in a long dataframe. Thinking ahead, it would be nice to already capitalize the house names for which I wrote a custom Capitalize() function.

# custom capitalization function
Capitalize = function(text){ 
  paste0(substring(text,1,1) %>% toupper(),
         substring(text,2))
}

# TO LONG FORMAT
houses_long <- houses_sentences %>%
  gather(key = house, value = test, -sentence, -.id) %>% 
  mutate(house = Capitalize(house)) %>% # capitalize names
  filter(test) %>% select(-test) # delete rows where house not referenced
# examine
houses_long %>%
  mutate(sentence = ifelse(nchar(sentence) > max.char, # cut off long sentences
                           paste0(substring(sentence, 1, max.char), "..."),
                           sentence)) %>% 
  head(20)
##                   .id                          sentence      house
## 1  Philosophers Stone i've been asking around, and i... Gryffindor
## 2  Philosophers Stone           "gryffindor," said ron. Gryffindor
## 3  Philosophers Stone "the four houses are called gr... Gryffindor
## 4  Philosophers Stone you might belong in gryffindor... Gryffindor
## 5  Philosophers Stone " brocklehurst, mandy" went to... Gryffindor
## 6  Philosophers Stone "finnigan, seamus," the sandy-... Gryffindor
## 7  Philosophers Stone                     "gryffindor!" Gryffindor
## 8  Philosophers Stone when it finally shouted, "gryf... Gryffindor
## 9  Philosophers Stone well, if you're sure -- better... Gryffindor
## 10 Philosophers Stone he took off the hat and walked... Gryffindor
## 11 Philosophers Stone "thomas, dean," a black boy ev... Gryffindor
## 12 Philosophers Stone harry crossed his fingers unde... Gryffindor
## 13 Philosophers Stone resident ghost of gryffindor t... Gryffindor
## 14 Philosophers Stone looking pleased at the stunned... Gryffindor
## 15 Philosophers Stone gryffindors have never gone so... Gryffindor
## 16 Philosophers Stone the gryffindor first years fol... Gryffindor
## 17 Philosophers Stone they all scrambled through it ... Gryffindor
## 18 Philosophers Stone nearly headless nick was alway... Gryffindor
## 19 Philosophers Stone professor mcgonagall was head ... Gryffindor
## 20 Philosophers Stone over the noise, snape said, "a... Gryffindor

Visualize House References

Woohoo, so tidy! Now comes the fun part: visualization. The following plots how often houses are mentioned overall, and in each book seperately.

# set plot width & height
w = 10; h = 6  

# PLOT REFERENCE FREQUENCY
houses_long %>%
  group_by(house) %>%
  summarize(n = n()) %>% # count sentences per house
  ggplot(aes(x = desc(house), y = n)) +
  geom_bar(aes(fill = house), stat = 'identity') +
  geom_text(aes(y = n / 2, label = house, col = house),  # center text
            size = 8, family = 'HP') +
  scale_fill_manual(values = houses_colors1) +
  scale_color_manual(values = houses_colors2) +
  theme(axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        legend.position = 'none') +
  labs(title = default_title,
       subtitle = "Combined references in all Harry Potter books",
       caption = default_caption,
       x = '', y = 'Name occurence') + 
  coord_flip()

barplot_house_occurance.png

# PLOT REFERENCE FREQUENCY OVER TIME 
houses_long %>%
  group_by(.id, house) %>%
  summarize(n = n()) %>% # count sentences per house per book
  ggplot(aes(x = .id, y = n, group = house)) +
  geom_line(aes(col = house), size = 2) +
  scale_color_manual(values = houses_colors1) +
  theme(legend.position = 'bottom',
        axis.text.x = element_text(angle = 15, hjust = 0.5, vjust = 0.5)) + # rotate x axis text
  labs(title = default_title, 
       subtitle = "References throughout the Harry Potter books",
       caption = default_caption,
       x = NULL, y = 'Name occurence', color = 'House') 

house_occurance_overtime.png

The Harry Potter font looks wonderful, right?

In terms of the data, Gryffindor and Slytherin definitely play a larger role in the Harry Potter stories. However, as the storyline progresses, Slytherin as a house seems to lose its importance. Their downward trend since the Chamber of Secrets results in Ravenclaw being mentioned more often in the final book (Edit – this is likely due to the diadem horcrux, as you will see later on).

I can’t but feel sorry for house Hufflepuff, which never really gets to involved throughout the saga.

Retrieve Reference Words & Data

Let’s dive into the specific words used in combination with each house. The following code retrieves and counts the single words used in the sentences where houses are mentioned.

# IDENTIFY WORDS USED IN COMBINATION WITH HOUSES
words_by_houses <- houses_long %>% 
  unnest_tokens(word, sentence, token = 'words') %>% # retrieve words
  mutate(word = gsub("'s", "", word)) %>% # remove possesive determiners
  group_by(house, word) %>% 
  summarize(word_n = n()) # count words per house
# examine
words_by_houses %>% head()
## # A tibble: 6 x 3
## # Groups:   house [1]
##        house        word word_n
##        <chr>       <chr>  <int>
## 1 Gryffindor         104      1
## 2 Gryffindor        22nd      1
## 3 Gryffindor           a    251
## 4 Gryffindor   abandoned      1
## 5 Gryffindor  abandoning      1
## 6 Gryffindor abercrombie      1

Visualize Word-House Combinations

Now we can visualize which words relate to each of the houses. Because facet_wrap() has trouble reordering the axes (because words may related to multiple houses in different frequencies), I needed some custom functionality, which I happily recycled from dgrtwo’s github. With these reorder_within() and scale_x_reordered() we can now make an ordered barplot of the top-20 most frequent words per house.

# custom functions for reordering facet plots
# https://github.com/dgrtwo/drlib/blob/master/R/reorder_within.R
reorder_within <- function(x, by, within, fun = mean, sep = "___", ...) {
  new_x <- paste(x, within, sep = sep)
  reorder(new_x, by, FUN = fun)
}

scale_x_reordered <- function(..., sep = "___") {
  reg <- paste0(sep, ".+$")
  ggplot2::scale_x_discrete(labels = function(x) gsub(reg, "", x), ...)
}

# set plot width & height
w = 10; h = 7; 

# PLOT MOST FREQUENT WORDS PER HOUSE
words_per_house = 20 # set number of top words
words_by_houses %>%
  group_by(house) %>%
  arrange(house, desc(word_n)) %>%
  mutate(top = row_number()) %>% # count word top position
  filter(top <= words_per_house) %>% # retain specified top number
  ggplot(aes(reorder_within(word, -top, house), # reorder by minus top number
             word_n, fill = house)) +
  geom_col(show.legend = F) +
  scale_x_reordered() + # rectify x axis labels 
  scale_fill_manual(values = houses_colors1) +
  scale_color_manual(values = houses_colors2) + 
  facet_wrap(~ house, scales = "free_y") + # facet wrap and free y axis
  coord_flip() +
  labs(title = default_title, 
       subtitle = "Words most commonly used together with houses",
       caption = default_caption,
       x = NULL, y = 'Word Frequency')

barplot_house_word_frequency2.png

Unsurprisingly, several stop words occur most frequently in the data. Intuitively, we would rerun the code but use dplyr::anti_join() on tidytext::stop_words to remove stop words.

# PLOT MOST FREQUENT WORDS PER HOUSE
# EXCLUDING STOPWORDS
words_by_houses %>%
  anti_join(stop_words, 'word') %>% # remove stop words
  group_by(house) %>% 
  arrange(house, desc(word_n)) %>%
  mutate(top = row_number()) %>% # count word top position
  filter(top <= words_per_house) %>% # retain specified top number
  ggplot(aes(reorder_within(word, -top, house), # reorder by minus top number
             word_n, fill = house)) +
  geom_col(show.legend = F) +
  scale_x_reordered() + # rectify x axis labels
  scale_fill_manual(values = houses_colors1) +
  scale_color_manual(values = houses_colors2) + 
  facet_wrap(~ house, scales = "free") + # facet wrap and free scales
  coord_flip() +
  labs(title = default_title, 
       subtitle = "Words most commonly used together with houses, excluding stop words",
       caption = default_caption,
       x = NULL, y = 'Word Frequency')

barplot_house_word_frequency_nostopwords2.png

However, some stop words have a different meaning in the Harry Potter universe. points are for instance quite informative to the Hogwarts houses but included in stop_words.

Moreover, many of the most frequent words above occur in relation to multiple or all houses. Take, for instance, Harry and Ron, which are in the top-10 of each house, or words like tablehouse, and professor.

We are more interested in words that describe one house, but not another. Similarly, we only want to exclude stop words which are really irrelevant. To this end, we compute a ratio-statistic below. This statistic displays how frequently a word occurs in combination with one house rather than with the others. However, we need to adjust this ratio for how often houses occur in the text as more text (and thus words) is used in reference to house Gryffindor than, for instance, Ravenclaw.

words_by_houses <- words_by_houses %>%
  group_by(word) %>% mutate(word_sum = sum(word_n)) %>% # counts words overall
  group_by(house) %>% mutate(house_n = n()) %>%
  ungroup() %>%
    # compute ratio of usage in combination with house as opposed to overall
  # adjusted for house references frequency as opposed to overall frequency
  mutate(ratio = (word_n / (word_sum - word_n + 1) / (house_n / n()))) 
# examine
words_by_houses %>% select(-word_sum, -house_n) %>% arrange(desc(word_n)) %>% head()
## # A tibble: 6 x 4
##        house       word word_n     ratio
##        <chr>      <chr>  <int>     <dbl>
## 1 Gryffindor        the   1057  2.373115
## 2  Slytherin        the    675  1.467926
## 3 Gryffindor gryffindor    602 13.076218
## 4 Gryffindor        and    477  2.197259
## 5 Gryffindor         to    428  2.830435
## 6 Gryffindor         of    362  2.213186
# PLOT MOST UNIQUE WORDS PER HOUSE BY RATIO
words_by_houses %>%
  group_by(house) %>%
  arrange(house, desc(ratio)) %>%
  mutate(top = row_number()) %>% # count word top position
  filter(top <= words_per_house) %>% # retain specified top number
  ggplot(aes(reorder_within(word, -top, house), # reorder by minus top number
             ratio, fill = house)) +
  geom_col(show.legend = F) +
  scale_x_reordered() + # rectify x axis labels
  scale_fill_manual(values = houses_colors1) +
  scale_color_manual(values = houses_colors2) + 
  facet_wrap(~ house, scales = "free") +  # facet wrap and free scales
  coord_flip() +
  labs(title = default_title, 
       subtitle = "Most informative words per house, by ratio",
       caption = default_caption,
       x = NULL, y = 'Adjusted Frequency Ratio (house vs. non-house)')

barplot_house_word_ratio.png

# PS. normally I would make a custom ggplot function 
#    when I plot three highly similar graphs

This ratio statistic (x-axis) should be interpreted as follows: night is used 29 times more often in combination with Gryffindor than with the other houses.

Do you think the results make sense:

  • Gryffindors spent dozens of hours during their afternoonsevenings, and nights in the, often emptytower room, apparently playing chess? Nevile Longbottom and Hermione Granger are Gryffindors, obviously, and Sirius Black is also on the list. The sword of Gryffindor is no surprise here either.
  • Hannah AbbotErnie Macmillan and Cedric Diggory are Hufflepuffs. Were they mostly hot curly blondes interested in herbology? Nevertheless, wild and aggresive seem unfitting for Hogwarts most boring house.
  • A lot of names on the list of Helena Ravenclaw’s house. Roger DaviesPadma Patil, Cho Chang, Miss S. FawcettStewart AckerleyTerry Boot, and Penelope Clearwater are indeed Ravenclaws, I believe. Ravenclaw’s Diadem was one of Voldemort horcruxes. AlectoCarrow, Death Eater by profession, was apparently sent on a mission by Voldemort to surprise Harry in Rawenclaw’s common room (source), which explains what she does on this list. Can anybody tell me what buststatue and spot have in relation to Ravenclaw?
  • House Slytherin is best represented by Gregory Goyle, one of the members of Draco Malfoy’s gang along with Vincent CrabbePansy Parkinson also represents house SlytherinSlytherin are famous for speaking Parseltongue and their house’s gem is an emerald. House Gaunt were pure-blood descendants from Salazar Slytherin and apparently Viktor Krum would not have misrepresented the Slytherin values either. Oh, and only the heir of Slytherin could control the monster in the Chamber of Secrets.

Honestly, I was not expecting such good results! However, there is always room for improvement.

We may want to exclude words that only occur once or twice in the book (e.g., Alecto) as well as the house names. Additionally, these barplots are not the optimal visualization if we would like to include more words per house. Fortunately, Hadley Wickham helped me discover treeplots. Let’s draw one using the ggfittext and the treemapify packages.

# set plot width & height
w = 12; h = 8; 

# PACKAGES FOR TREEMAP
# devtools::install_github("wilkox/ggfittext")
# devtools::install_github("wilkox/treemapify")
library(ggfittext)
library(treemapify)


# PLOT MOST UNIQUE WORDS PER HOUSE BY RATIO
words_by_houses %>%
  filter(word_n > 3) %>% # filter words with few occurances
  filter(!grepl(regex_houses, word)) %>% # exclude house names
  group_by(house) %>%
  arrange(house, desc(ratio), desc(word_n)) %>%
  mutate(top = seq_along(ratio)) %>%
  filter(top <= words_per_house) %>% # filter top n words
  ggplot(aes(area = ratio, label = word, subgroup = house, fill = house)) +
  geom_treemap() + # create treemap
  geom_treemap_text(aes(col = house), family = "HP", place = 'center') + # add text
  geom_treemap_subgroup_text(aes(col = house), # add house names
                             family = "HP", place = 'center', alpha = 0.3, grow = T) +
  geom_treemap_subgroup_border(colour = 'black') +
  scale_fill_manual(values = houses_colors1) +
  scale_color_manual(values = houses_colors2) + 
  theme(legend.position = 'none') +
  labs(title = default_title, 
       subtitle = "Most informative words per house, by ratio",
       caption = default_caption)

treemap_house_word_ratio

A treemap can display more words for each of the houses and displays their relative proportions better. New words regarding the houses include the following, but do you see any others?

  • Slytherin girls laugh out loud whereas Ravenclaw had a few little, pretty girls?
  • Gryffindors, at least Harry and his friends, got in trouble often, that is a fact.
  • Yellow is the color of house Hufflepuff whereas Slytherin is green indeed.
  • Zacherias Smith joined Hufflepuff and Luna Lovegood Ravenclaw.
  • Why is Voldemort in camp Ravenclaw?!

In the earlier code, we specified a minimum number of occurances for words to be included, which is a bit hacky but necessary to make the ratio statistic work as intended. Foruntately, there are other ways to estimate how unique or informative words are to houses that do not require such hacks.

TF-IDF

tf-idf similarly estimates how unique / informative words are for a body of text (for more info: Wikipedia). We can calculate a tf-idf score for each word within each document (in our case house texts) by taking the product of two statistics:

  • TF or term frequency, meaning the number of times the word occurs in a document.
  • IDF or inverse document frequency, specifically the logarithm of the inverse number of documents the word occurs in.

A high tf-idf score means that a word occurs relatively often in a specific document and not often in other documents. Different weighting schemes can be used to td-idf’s performance in different settings but we used the simple default of tidytext::bind_tf_idf().

An advantage of tf-idf over the earlier ratio statistic is that we no longer need to specify a minimum frequency: low frequency words will have low tf and thus low tf-idf. A disadvantage is that tf-idf will automatically disregard words occur together with each house, be it only once: these words have zero idf (log(4/4)) so zero tf-idf.

Let’s run the treemap gain, but not on the computed tf-idf scores.

words_by_houses <- words_by_houses %>%
  # compute term frequency and inverse document frequency
  bind_tf_idf(word, house, word_n)
# examine
words_by_houses %>% select(-house_n) %>% head()
## # A tibble: 6 x 8
##        house        word word_n word_sum    ratio           tf       idf
##        <chr>       <chr>  <int>    <int>    <dbl>        <dbl>     <dbl>
## 1 Gryffindor         104      1        1 2.671719 6.488872e-05 1.3862944
## 2 Gryffindor        22nd      1        1 2.671719 6.488872e-05 1.3862944
## 3 Gryffindor           a    251      628 1.774078 1.628707e-02 0.0000000
## 4 Gryffindor   abandoned      1        1 2.671719 6.488872e-05 1.3862944
## 5 Gryffindor  abandoning      1        2 1.335860 6.488872e-05 0.6931472
## 6 Gryffindor abercrombie      1        1 2.671719 6.488872e-05 1.3862944
## # ... with 1 more variables: tf_idf <dbl>
# PLOT MOST UNIQUE WORDS PER HOUSE BY TF_IDF
words_per_house = 30
words_by_houses %>%
  filter(tf_idf > 0) %>% # filter for zero tf_idf
  group_by(house) %>%
  arrange(house, desc(tf_idf), desc(word_n)) %>%
  mutate(top = seq_along(tf_idf)) %>%
  filter(top <= words_per_house) %>%
  ggplot(aes(area = tf_idf, label = word, subgroup = house, fill = house)) +
  geom_treemap() + # create treemap
  geom_treemap_text(aes(col = house), family = "HP", place = 'center') + # add text
  geom_treemap_subgroup_text(aes(col = house), # add house names
                             family = "HP", place = 'center', alpha = 0.3, grow = T) +
  geom_treemap_subgroup_border(colour = 'black') +
  scale_fill_manual(values = houses_colors1) +
  scale_color_manual(values = houses_colors2) + 
  theme(legend.position = 'none') +
  labs(title = default_title, 
       subtitle = "Most informative words per house, by tf-idf",
       caption = default_caption)

treemap_house_word_tfidf.png

This plot looks quite different from its predecessor. For instance, Marcus Flint and Adrian Pucey are added to house Slytherin and Hufflepuff’s main color is indeed not just yellow, but canary yellow. Severus Snape’s dual role is also nicely depicted now, with him in both house Slytherin and house Gryffindor. Do you notice any other important differences? Did we lose any important words because they occured in each of our four documents?

House Personality Profiles (by NRC Sentiment Analysis)

We end this second Harry Plotter blog by examining to what the extent the stereotypes that exist of the Hogwarts Houses can be traced back to the books. To this end, we use the NRC sentiment dictionary, see also the the previous blog, with which we can estimate to what extent the most informative words for houses (we have over a thousand for each house) relate to emotions such as anger, fear, or trust.

The code below retains only the emotion words in our words_by_houses dataset and multiplies their tf-idf scores by their relative frequency, so that we retrieve one score per house per sentiment.

# PLOT SENTIMENT OF INFORMATIVE WORDS (TFIDF)
words_by_houses %>%
  inner_join(get_sentiments("nrc"), by = 'word') %>%
  group_by(house, sentiment) %>%
  summarize(score = sum(word_n / house_n * tf_idf)) %>% # compute emotion score
  ggplot(aes(x = house, y = score, group = house)) +
  geom_col(aes(fill = house)) + # create barplots
  geom_text(aes(y = score / 2, label = substring(house, 1, 1), col = house), 
            family = "HP", vjust = 0.5) + # add house letter in middle
  facet_wrap(~ Capitalize(sentiment), scales = 'free_y') + # facet and free y axis
  scale_fill_manual(values = houses_colors1) +
  scale_color_manual(values = houses_colors2) + 
  theme(legend.position = 'none', # tidy dataviz
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        strip.text.x = element_text(colour = 'black', size = 12)) +
  labs(title = default_title, 
       subtitle = "Sentiment (nrc) related to houses' informative words (tf-idf)",
       caption = default_caption,
       y = "Sentiment score", x = NULL)

barplot_sentiment_house_tfidf.png

The results to a large extent confirm the stereotypes that exist regarding the Hogwarts houses:

  • Gryffindors are full of anticipation and the most positive and trustworthy.
  • Hufflepuffs are the most joyous but not extraordinary on any other front.
  • Ravenclaws are distinguished by their low scores. They are super not-angry and relatively not-anticipating, not-negative, and not-sad.
  • Slytherins are the angriest, the saddest, and the most feared and disgusting. However, they are also relatively joyous (optimistic?) and very surprising (shocking?).

Conclusion and future work

With this we have come to the end of the second part of the Harry Plotter project, in which we used tf-idf and ratio statistics to examine which words were most informative / unique to each of the houses of Hogwarts. The data was retrieved using the harrypotter package and transformed using tidytext and the tidyverse. Visualizations were made with ggplot2 and treemapify, using a Harry Potter font.

I have several ideas for subsequent posts and I’d love to hear your preferences or suggestions:

  • I would like to demonstrate how regular expressions can be used to retrieve (sub)strings that follow a specific format. We could use regex to examine, for instance, when, and by whom, which magical spells are cast.
  • I would like to use network analysis to examine the interactions between the characters. We could retrieve networks from the books and conduct sentiment analysis to establish the nature of relationships. Similarly, we could use unsupervised learning / clustering to explore character groups.
  • I would like to use topic models, such as latent dirichlet allocation, to identify the main topics in the books. We could, for instance, try to summarize each book chapter in single sentence, or examine how topics (e.g., love or death) build or fall over time.
  • Finally, I would like to build an interactive application / dashboard in Shiny (another hobby of mine) so that readers like you can explore patterns in the books yourself. Unfortunately, the free on shinyapps.io only 25 hosting hours per month : (

For now, I hope you enjoyed this blog and that you’ll be back for more. To receive new content first, please subscribe to my website www.paulvanderlaken.com, follow me on Twitter, or add me on LinkedIn.

If you would like to contribute to, collaborate on, or need assistance with a data science project or venture, please feel free to reach out

Scraping RStudio blogs to establish how “pleased” Hadley Wickham is.

Scraping RStudio blogs to establish how “pleased” Hadley Wickham is.

This is reposted from DavisVaughan.com with minor modifications.

Introduction

A while back, I saw a conversation on twitter about how Hadley uses the word “pleased” very often when introducing a new blog post (I couldn’t seem to find this tweet anymore. Can anyone help?). Out of curiosity, and to flex my R web scraping muscles a bit, I’ve decided to analyze the 240+ blog posts that RStudio has put out since 2011. This post will do a few things:

  • Scrape the RStudio blog archive page to construct URL links to each blog post
  • Scrape the blog post text and metadata from each post
  • Use a bit of tidytext for some exploratory analysis
  • Perform a statistical test to compare Hadley’s use of “pleased” to the other blog post authors

Spoiler alert: Hadley uses “pleased” ALOT.

Required packages

library(tidyverse)
library(tidytext)
library(rvest)
library(xml2)

Extract the HTML from the RStudio blog archive

To be able to extract the text from each blog post, we first need to have a link to that blog post. Luckily, RStudio keeps an up to date archive page that we can scrape. Using xml2, we can get the HTML off that page.

archive_page <- "https://blog.rstudio.com/archives/"

archive_html <- read_html(archive_page)

# Doesn't seem very useful...yet
archive_html
## {xml_document}
## <html lang="en-us">
## [1] <head>\n<meta http-equiv="Content-Type" content="text/html; charset= ...
## [2] <body>\n    <nav class="menu"><svg version="1.1" xmlns="http://www.w ...

Now we use a bit of rvest magic combined with the HTML inspector in Chrome to figure out which elements contain the info we need (I also highly recommend SelectorGadget for this kind of work). Looking at the image below, you can see that all of the links are contained within the main tag as a tags (links).

The code below extracts all of the links, and then adds the prefix containing the base URL of the site.

links <- archive_html %>%
  
  # Only the "main" body of the archive
  html_nodes("main") %>%
  
  # Grab any node that is a link
  html_nodes("a") %>%
  
  # Extract the hyperlink reference from those link tags
  # The hyperlink is an attribute as opposed to a node
  html_attr("href") %>%
  
  # Prefix them all with the base URL
  paste0("http://blog.rstudio.com", .)

head(links)
## [1] "http://blog.rstudio.com/2017/08/16/rstudio-preview-connections/"             
## [2] "http://blog.rstudio.com/2017/08/15/contributed-talks-diversity-scholarships/"
## [3] "http://blog.rstudio.com/2017/08/11/rstudio-v1-1-preview-terminal/"           
## [4] "http://blog.rstudio.com/2017/08/10/upcoming-workshops/"                      
## [5] "http://blog.rstudio.com/2017/08/03/rstudio-connect-v1-5-4-plumber/"          
## [6] "http://blog.rstudio.com/2017/07/31/sparklyr-0-6/"

HTML from each blog post

Now that we have every link, we’re ready to extract the HTML from each individual blog post. To make things more manageable, we start by creating a tibble, and then using the mutate + map combination to created a column of XML Nodesets (we will use this combination a lot). Each nodeset contains the HTML for that blog post (exactly like the HTML for the archive page).

blog_data <- tibble(links)

blog_data <- blog_data %>%
  mutate(main = map(
                    # Iterate through every link
                    .x = links, 
                    
                    # For each link, read the HTML for that page, and return the main section 
                    .f = ~read_html(.) %>%
                            html_nodes("main")
                    )
         )

select(blog_data, main)
## # A tibble: 249 x 1
##                 main
##               <list>
##  1 <S3: xml_nodeset>
##  2 <S3: xml_nodeset>
##  3 <S3: xml_nodeset>
##  4 <S3: xml_nodeset>
##  5 <S3: xml_nodeset>
##  6 <S3: xml_nodeset>
##  7 <S3: xml_nodeset>
##  8 <S3: xml_nodeset>
##  9 <S3: xml_nodeset>
## 10 <S3: xml_nodeset>
## # ... with 239 more rows
blog_data$main[1]
## [[1]]
## {xml_nodeset (1)}
## [1] <main><div class="article-meta">\n<h1><span class="title">RStudio 1. ...

Meta information

Before extracting the blog post itself, lets grab the meta information about each post, specifically:

  • Author
  • Title
  • Date
  • Category
  • Tags

In the exploratory analysis, we will use author and title, but the other information might be useful for future analysis.

Looking at the first blog post, the Author, Date, and Title are all HTML class names that we can feed into rvest to extract that information.

In the code below, an example of extracting the author information is shown. To select a HTML class (like “author”) as opposed to a tag (like “main”), we have to put a period in front of the class name. Once the html node we are interested in has been identified, we can extract the text for that node using html_text().

blog_data$main[[1]] %>%
  html_nodes(".author") %>%
  html_text()
## [1] "Jonathan McPherson"

To scale up to grab the author for all posts, we use map_chr() since we want a character of the author’s name returned.

map_chr(.x = blog_data$main,
        .f = ~html_nodes(.x, ".author") %>%
                html_text()) %>%
  head(10)
##  [1] "Jonathan McPherson" "Hadley Wickham"     "Gary Ritchie"      
##  [4] "Roger Oberg"        "Jeff Allen"         "Javier Luraschi"   
##  [7] "Hadley Wickham"     "Roger Oberg"        "Garrett Grolemund" 
## [10] "Hadley Wickham"

Finally, notice that if we switch ".author" with ".title" or ".date" then we can grab that information as well. This kind of thinking means that we should create a function for extracting these pieces of information!

extract_info <- function(html, class_name) {
  map_chr(
          # Given the list of main HTMLs
          .x = html,
          
          # Extract the text we are interested in for each one 
          .f = ~html_nodes(.x, class_name) %>%
                  html_text())
}

# Extract the data
blog_data <- blog_data %>%
  mutate(
     author = extract_info(main, ".author"),
     title  = extract_info(main, ".title"),
     date   = extract_info(main, ".date")
    )

select(blog_data, author, date)
## # A tibble: 249 x 2
##                author       date
##                 <chr>      <chr>
##  1 Jonathan McPherson 2017-08-16
##  2     Hadley Wickham 2017-08-15
##  3       Gary Ritchie 2017-08-11
##  4        Roger Oberg 2017-08-10
##  5         Jeff Allen 2017-08-03
##  6    Javier Luraschi 2017-07-31
##  7     Hadley Wickham 2017-07-13
##  8        Roger Oberg 2017-07-12
##  9  Garrett Grolemund 2017-07-11
## 10     Hadley Wickham 2017-06-27
## # ... with 239 more rows
select(blog_data, title)
## # A tibble: 249 x 1
##                                                                          title
##                                                                          <chr>
##  1                                      RStudio 1.1 Preview - Data Connections
##  2 rstudio::conf(2018): Contributed talks, e-posters, and diversity scholarshi
##  3                                              RStudio v1.1 Preview: Terminal
##  4                                                Building tidy tools workshop
##  5                            RStudio Connect v1.5.4 - Now Supporting Plumber!
##  6                                                                sparklyr 0.6
##  7                                                                 haven 1.1.0
##  8                                   Registration open for rstudio::conf 2018!
##  9                                                          Introducing learnr
## 10                                                                dbplyr 1.1.0
## # ... with 239 more rows

Categories and tags

The other bits of meta data that might be interesting are the categories and tags that the post falls under. This is a little bit more involved, because both the categories and tags fall under the same class, ".terms". To separate them, we need to look into the href to see if the information is either a tag or a category (href = “/categories/” VS href = “/tags/”).

The function below extracts either the categories or the tags, depending on the argument, by:

  • Extracting the ".terms" class, and then all of the links inside of it (a tags).
  • Checking each link to see if the hyperlink reference contains “categories” or “tags” depending on the one that we are interested in. If it does, it returns the text corresponding to that link, otherwise it returns NAs which are then removed.

The final step results in two list columns containing character vectors of varying lengths corresponding to the categories and tags of each post.

extract_tag_or_cat <- function(html, info_name) {
  
  # Extract the links under the terms class
  cats_and_tags <- map(.x = html, 
                       .f = ~html_nodes(.x, ".terms") %>%
                              html_nodes("a"))
  
  # For each link, if the href contains the word categories/tags 
  # return the text corresponding to that link
  map(cats_and_tags, 
    ~if_else(condition = grepl(info_name, html_attr(.x, "href")), 
             true      = html_text(.x), 
             false     = NA_character_) %>%
      .[!is.na(.)])
}

# Apply our new extraction function
blog_data <- blog_data %>%
  mutate(
    categories = extract_tag_or_cat(main, "categories"),
    tags       = extract_tag_or_cat(main, "tags")
  )

select(blog_data, categories, tags)
## # A tibble: 249 x 2
##    categories       tags
##        <list>     <list>
##  1  <chr [1]>  <chr [0]>
##  2  <chr [1]>  <chr [0]>
##  3  <chr [1]>  <chr [3]>
##  4  <chr [3]>  <chr [8]>
##  5  <chr [3]>  <chr [2]>
##  6  <chr [1]>  <chr [3]>
##  7  <chr [2]>  <chr [0]>
##  8  <chr [4]> <chr [13]>
##  9  <chr [2]>  <chr [2]>
## 10  <chr [2]>  <chr [0]>
## # ... with 239 more rows
blog_data$categories[4]
## [[1]]
## [1] "Packages"  "tidyverse" "Training"
blog_data$tags[4]
## [[1]]
## [1] "Advanced R"       "data science"     "ggplot2"         
## [4] "Hadley Wickham"   "R"                "RStudio Workshop"
## [7] "r training"       "tutorial"

The blog post itself

Finally, to extract the blog post itself, we can notice that each piece of text in the post is inside of a paragraph tag (p). Being careful to avoid the ".terms" class that contained the categories and tags, which also happens to be in a paragraph tag, we can extract the full blog posts. To ignore the ".terms" class, use the :not() selector.

blog_data <- blog_data %>%
  mutate(
    text = map_chr(main, ~html_nodes(.x, "p:not(.terms)") %>%
                 html_text() %>%
                 # The text is returned as a character vector. 
                 # Collapse them all into 1 string.
                 paste0(collapse = " "))
  )

select(blog_data, text)
## # A tibble: 249 x 1
##                                                                           text
##                                                                          <chr>
##  1 Today, we’re continuing our blog series on new features in RStudio 1.1. If 
##  2 rstudio::conf, the conference on all things R and RStudio, will take place 
##  3 Today we’re excited to announce availability of our first Preview Release f
##  4 Have you embraced the tidyverse? Do you now want to expand it to meet your 
##  5 We’re thrilled to announce support for hosting Plumber APIs in RStudio Conn
##  6 We’re excited to announce a new release of the sparklyr package, available 
##  7 "I’m pleased to announce the release of haven 1.1.0. Haven is designed to f
##  8 RStudio is very excited to announce that rstudio::conf 2018 is open for reg
##  9 We’re pleased to introduce the learnr package, now available on CRAN. The l
## 10 "I’m pleased to announce the release of the dbplyr package, which now conta
## # ... with 239 more rows

Who writes the most posts?

Now that we have all of this data, what can we do with it? To start with, who writes the most posts?

blog_data %>%
  group_by(author) %>%
  summarise(count = n()) %>%
  mutate(author = reorder(author, count)) %>%
  
  # Create a bar graph of author counts
  ggplot(mapping = aes(x = author, y = count)) + 
  geom_col() +
  coord_flip() +
  labs(title    = "Who writes the most RStudio blog posts?",
       subtitle = "By a huge margin, Hadley!") +
  # Shoutout to Bob Rudis for the always fantastic themes
  hrbrthemes::theme_ipsum(grid = "Y")

Tidytext

I’ve never used tidytext before today, but to get our feet wet, let’s create a tokenized tidy version of our data. By using unnest_tokens() the data will be reshaped to a long format holding 1 word per row, for each blog post. This tidy format lends itself to all manner of analysis, and a number of them are outlined in Julia Silge and David Robinson’s Text Mining with R.

tokenized_blog <- blog_data %>%
  select(title, author, date, text) %>%
  unnest_tokens(output = word, input = text)

select(tokenized_blog, title, word)
## # A tibble: 84,542 x 2
##                                     title       word
##                                     <chr>      <chr>
##  1 RStudio 1.1 Preview - Data Connections      today
##  2 RStudio 1.1 Preview - Data Connections      we’re
##  3 RStudio 1.1 Preview - Data Connections continuing
##  4 RStudio 1.1 Preview - Data Connections        our
##  5 RStudio 1.1 Preview - Data Connections       blog
##  6 RStudio 1.1 Preview - Data Connections     series
##  7 RStudio 1.1 Preview - Data Connections         on
##  8 RStudio 1.1 Preview - Data Connections        new
##  9 RStudio 1.1 Preview - Data Connections   features
## 10 RStudio 1.1 Preview - Data Connections         in
## # ... with 84,532 more rows

Remove stop words

A number of words like “a” or “the” are included in the blog that don’t really add value to a text analysis. These stop words can be removed using an anti_join() with the stop_words dataset that comes with tidytext. After removing stop words, the number of rows was cut in half!

tokenized_blog <- tokenized_blog %>%
  anti_join(stop_words, by = "word") %>%
  arrange(desc(date))

select(tokenized_blog, title, word)
## # A tibble: 39,768 x 2
##                                     title            word
##                                     <chr>           <chr>
##  1 RStudio 1.1 Preview - Data Connections          server
##  2 RStudio 1.1 Preview - Data Connections          here’s
##  3 RStudio 1.1 Preview - Data Connections           isn’t
##  4 RStudio 1.1 Preview - Data Connections straightforward
##  5 RStudio 1.1 Preview - Data Connections             pro
##  6 RStudio 1.1 Preview - Data Connections         command
##  7 RStudio 1.1 Preview - Data Connections         console
##  8 RStudio 1.1 Preview - Data Connections           makes
##  9 RStudio 1.1 Preview - Data Connections           makes
## 10 RStudio 1.1 Preview - Data Connections          you’re
## # ... with 39,758 more rows

Top 15 words overall

Out of pure curiousity, what are the top 15 words for all of the blog posts?

tokenized_blog %>%
  count(word, sort = TRUE) %>%
  slice(1:15) %>%
  mutate(word = reorder(word, n)) %>%
  
  ggplot(aes(word, n)) +
  geom_col() + 
  coord_flip() + 
  labs(title = "Top 15 words overall") +
  hrbrthemes::theme_ipsum(grid = "Y")

Is Hadley more “pleased” than everyone else?

As mentioned at the beginning of the post, Hadley apparently uses the word “pleased” in his blog posts an above average number of times. Can we verify this statistically?

Our null hypothesis is that the proportion of blog posts that use the word “pleased” written by Hadley is less than or equal to the proportion of those written by the rest of the RStudio team.

More simply, our null is that Hadley uses “pleased” less than or the same as the rest of the team.

Let’s check visually to compare the two groups of posts.

pleased <- tokenized_blog %>%
  
  # Group by blog post
  group_by(title) %>%
  
  # If the blog post contains "pleased" put yes, otherwise no
  # Add a column checking if the author was Hadley
  mutate(
    contains_pleased = case_when(
      "pleased" %in% word ~ "Yes",
      TRUE                ~ "No"),
    
    is_hadley = case_when(
      author == "Hadley Wickham" ~ "Hadley",
      TRUE                       ~ "Not Hadley")
    ) %>%
  
  # Remove all duplicates now
  distinct(title, contains_pleased, is_hadley)

pleased %>%
  ggplot(aes(x = contains_pleased)) +
  geom_bar() +
  facet_wrap(~is_hadley, scales = "free_y") +
  labs(title    = "Does this blog post contain 'pleased'?", 
       subtitle = "Nearly half of Hadley's do!",
       x        = "Contains 'pleased'",
       y        = "Count") +
  hrbrthemes::theme_ipsum(grid = "Y")

Is there a statistical difference here?

To check if there is a statistical difference, we will use a test for difference in proportions contained in the R function, prop.test(). First, we need a continency table of the counts. Given the current form of our dataset, this isn’t too hard with the table() function from base R.

contingency_table <- pleased %>%
  ungroup() %>%
  select(is_hadley, contains_pleased) %>%
  # Order the factor so Yes is before No for easy interpretation
  mutate(contains_pleased = factor(contains_pleased, levels = c("Yes", "No"))) %>%
  table()

contingency_table
##             contains_pleased
## is_hadley    Yes  No
##   Hadley      43  45
##   Not Hadley  17 144

From our null hypothesis, we want to perform a one sided test. The alternative to our null is that Hadley uses “pleased” more than the rest of the RStudio team. For this reason, we specify alternative = "greater".

test_prop <- contingency_table %>%
  prop.test(alternative = "greater")

test_prop
## 
##  2-sample test for equality of proportions with continuity
##  correction
## 
## data:  .
## X-squared = 43.575, df = 1, p-value = 2.04e-11
## alternative hypothesis: greater
## 95 percent confidence interval:
##  0.2779818 1.0000000
## sample estimates:
##    prop 1    prop 2 
## 0.4886364 0.1055901

We could also tidy this up with broom if we were inclined to.

broom::tidy(test_prop)
##   estimate1 estimate2 statistic      p.value parameter  conf.low conf.high
## 1 0.4886364 0.1055901  43.57517 2.039913e-11         1 0.2779818         1
##                                                                 method
## 1 2-sample test for equality of proportions with continuity correction
##   alternative
## 1     greater

Test conclusion

  • 48.86% of Hadley’s posts contain “pleased”
  • 10.56% of the rest of the RStudio team’s posts contain “pleased”
  • With a p-value of 2.04e-11, we reject the null that Hadley uses “pleased” less than or the same as the rest of the team. The evidence supports the idea that he has a much higher preference for it!

Hadley uses “pleased” quite a bit!

About the author

Davis Vaughan is a Master’s student studying Mathematical Finance at the University of North Carolina at Charlotte. He is the other half of Business Science. We develop R packages for financial analysis. Additionally, we have a network of data scientists at our disposal to bring together the best team to work on consulting projects. Check out our website to learn more! He is the coauthor of R packages tidyquant and timetk.

R resources (free courses, books, tutorials, & cheat sheets)

R resources (free courses, books, tutorials, & cheat sheets)

Help yourself to these free books, tutorials, packages, cheat sheets, and many more materials for R programming. There’s a separate overview for handy R programming tricks. If you have additions, please comment below or contact me!


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LAST UPDATED: 2020-06-29


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  • coefplot – visualizes model statistics
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Text Mining: Shirin’s Twitter Feed

Text mining and analytics, natural language processing, and topic modelling have definitely become sort of an obsession of mine. I am just amazed by the insights one can retrieve from textual information, and with the ever increasing amounts of unstructured data on the internet, recreational analysts are coming up with the most amazing text mining projects these days.

Only last week, I came across posts talking about how the text in the Game of Thrones books to demonstrate a gender bias, how someone created an entire book with weirdly-satisfying computer-generated poems, and how to conduct a rather impressive analysis of your Twitter following. The latter, I copied below, with all props obviously for Shirin – the author.

For those of you who want to learn more about text mining and, specifically, how to start mining in R with tidytext, an new text-mining complement to the tidyverse, I can strongly recommend the new book by Julia Silge and David Robinson. This book has helped me greatly in learning the basics and you can definitely expect some blogs on my personal text mining projects soon.

===== COPIED FROM SHIRIN’S PLAYGROUND =====

Lately, I have been more and more taken with tidy principles of data analysis. They are elegant and make analyses clearer and easier to comprehend. Following the tidyverse and ggraph, I have been quite intrigued by applying tidy principles to text analysis with Julia Silge and David Robinson’s tidytext.

In this post, I will explore tidytext with an analysis of my Twitter followers’ descriptions to try and learn more about the people who are interested in my tweets, which are mainly about Data Science and Machine Learning.

Resources I found useful for this analysis were http://www.rdatamining.com/docs/twitter-analysis-with-r and http://tidytextmining.com/tidytext.html

Retrieving Twitter data

I am using twitteR to retrieve data from Twitter (I have also tried rtweet but for some reason, my API key, secret and token (that worked with twitteR) resulted in a “failed to authorize” error with rtweet’s functions).

library(twitteR)

Once we have set up our Twitter REST API, we get the necessary information to authenticate our access.

consumerKey = "INSERT KEY HERE"
consumerSecret = "INSERT SECRET KEY HERE"
accessToken = "INSERT TOKEN HERE"
accessSecret = "INSERT SECRET TOKEN HERE"
options(httr_oauth_cache = TRUE)

setup_twitter_oauth(consumer_key = consumerKey, 
                    consumer_secret = consumerSecret, 
                    access_token = accessToken, 
                    access_secret = accessSecret)

Now, we can access information from Twitter, like timeline tweets, user timelines, mentions, tweets & retweets, followers, etc.

All the following datasets were retrieved on June 7th 2017, converted to a data frame for tidy analysis and saved for later use:

  • the last 3200 tweets on my timeline
my_name <- userTimeline("ShirinGlander", n = 3200, includeRts=TRUE)
my_name_df <- twListToDF(my_name)
save(my_name_df, file = "my_name.RData")
  • my last 3200 mentions and retweets
my_mentions <- mentions(n = 3200)
my_mentions_df <- twListToDF(my_mentions)
save(my_mentions_df, file = "my_mentions.RData")

my_retweets <- retweetsOfMe(n = 3200)
my_retweets_df <- twListToDF(my_retweets)
save(my_retweets_df, file = "my_retweets.RData")
  • the last 3200 tweets to me
tweetstome <- searchTwitter("@ShirinGlander", n = 3200)
tweetstome_df <- twListToDF(tweetstome)
save(tweetstome_df, file = "tweetstome.RData")
  • my friends and followers
user <- getUser("ShirinGlander")

friends <- user$getFriends() # who I follow
friends_df <- twListToDF(friends)
save(friends_df, file = "my_friends.RData")

followers <- user$getFollowers() # my followers
followers_df <- twListToDF(followers)
save(followers_df, file = "my_followers.RData")

Analyzing friends and followers

In this post, I will have a look at my friends and followers.

load("my_friends.RData")
load("my_followers.RData")

I am going to use packages from the tidyverse (tidyquant for plotting).

library(tidyverse)
library(tidyquant)
  • Number of friends (who I follow on Twitter): 225
  • Number of followers (who follows me on Twitter): 324
  • Number of friends who are also followers: 97

What languages do my followers speak?

One of the columns describing my followers is which language they have set for their Twitter account. Not surprisingly, English is by far the most predominant language of my followers, followed by German, Spanish and French.

followers_df %>%
  count(lang) %>%
  droplevels() %>%
  ggplot(aes(x = reorder(lang, desc(n)), y = n)) +
    geom_bar(stat = "identity", color = palette_light()[1], fill = palette_light()[1], alpha = 0.8) +
    theme_tq() +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
    labs(x = "language ISO 639-1 code",
         y = "number of followers")

Who are my most “influential” followers (i.e. followers with the biggest network)?

I also have information about the number of followers that each of my followers have (2nd degree followers). Most of my followers are followed by up to ~ 1000 people, while only a few have a very large network.

followers_df %>%
  ggplot(aes(x = log2(followersCount))) +
    geom_density(color = palette_light()[1], fill = palette_light()[1], alpha = 0.8) +
    theme_tq() +
    labs(x = "log2 of number of followers",
         y = "density")

How active are my followers (i.e. how often do they tweet)

The followers data frame also tells me how many statuses (i.e. tweets) each of followers have. To make the numbers comparable, I am normalizing them by the number of days that they have had their accounts to calculate the average number of tweets per day.

followers_df %>%
  mutate(date = as.Date(created, format = "%Y-%m-%d"),
         today = as.Date("2017-06-07", format = "%Y-%m-%d"),
         days = as.numeric(today - date),
         statusesCount_pDay = statusesCount / days) %>%
  ggplot(aes(x = log2(statusesCount_pDay))) +
    geom_density(color = palette_light()[1], fill = palette_light()[1], alpha = 0.8) +
    theme_tq()

Who are my followers with the biggest network and who tweet the most?

followers_df %>%
  mutate(date = as.Date(created, format = "%Y-%m-%d"),
         today = as.Date("2017-06-07", format = "%Y-%m-%d"),
         days = as.numeric(today - date),
         statusesCount_pDay = statusesCount / days) %>%
  select(screenName, followersCount, statusesCount_pDay) %>%
  arrange(desc(followersCount)) %>%
  top_n(10)
##         screenName followersCount statusesCount_pDay
## 1        dr_morton         150937           71.35193
## 2    Scientists4EU          66117           17.64389
## 3       dr_morton_          63467           46.57763
## 4   NewScienceWrld          60092           54.65874
## 5     RubenRabines          42286           25.99592
## 6  machinelearnbot          27427          204.67061
## 7  BecomingDataSci          16807           25.24069
## 8       joelgombin           6566           21.24094
## 9    renato_umeton           1998           19.58387
## 10 FranPatogenLoco            311           28.92593
followers_df %>%
  mutate(date = as.Date(created, format = "%Y-%m-%d"),
         today = as.Date("2017-06-07", format = "%Y-%m-%d"),
         days = as.numeric(today - date),
         statusesCount_pDay = statusesCount / days) %>%
  select(screenName, followersCount, statusesCount_pDay) %>%
  arrange(desc(statusesCount_pDay)) %>%
  top_n(10)
##         screenName followersCount statusesCount_pDay
## 1  machinelearnbot          27427          204.67061
## 2        dr_morton         150937           71.35193
## 3   NewScienceWrld          60092           54.65874
## 4       dr_morton_          63467           46.57763
## 5  FranPatogenLoco            311           28.92593
## 6     RubenRabines          42286           25.99592
## 7  BecomingDataSci          16807           25.24069
## 8       joelgombin           6566           21.24094
## 9    renato_umeton           1998           19.58387
## 10   Scientists4EU          66117           17.64389

Is there a correlation between number of followers and number of tweets?

Indeed, there seems to be a correlation that users with many followers also tend to tweet more often.

followers_df %>%
  mutate(date = as.Date(created, format = "%Y-%m-%d"),
         today = as.Date("2017-06-07", format = "%Y-%m-%d"),
         days = as.numeric(today - date),
         statusesCount_pDay = statusesCount / days) %>%
  ggplot(aes(x = followersCount, y = statusesCount_pDay, color = days)) +
    geom_smooth(method = "lm") +
    geom_point() +
    scale_color_continuous(low = palette_light()[1], high = palette_light()[2]) +
    theme_tq()

Tidy text analysis

Next, I want to know more about my followers by analyzing their Twitter descriptions with the tidytext package.

library(tidytext)
library(SnowballC)

To prepare the data, I am going to unnest the words (or tokens) in the user descriptions, convert them to the word stem, remove stop words and urls.

data(stop_words)

tidy_descr <- followers_df %>%
  unnest_tokens(word, description) %>%
  mutate(word_stem = wordStem(word)) %>%
  anti_join(stop_words, by = "word") %>%
  filter(!grepl("\\.|http", word))

What are the most commonly used words in my followers’ descriptions?

The first question I want to ask is what words are most common in my followers’ descriptions.

Not surprisingly, the most common word is “data”. I do tweet mostly about data related topics, so it makes sense that my followers are mostly likeminded. The rest is also related to data science, machine learning and R.

tidy_descr %>%
  count(word_stem, sort = TRUE) %>%
  filter(n > 20) %>%
  ggplot(aes(x = reorder(word_stem, n), y = n)) +
    geom_col(color = palette_light()[1], fill = palette_light()[1], alpha = 0.8) +
    coord_flip() +
    theme_tq() +
    labs(x = "",
         y = "count of word stem in all followers' descriptions")

This, we can also show with a word cloud.

library(wordcloud)
library(tm)
tidy_descr %>%
  count(word_stem) %>%
  mutate(word_stem = removeNumbers(word_stem)) %>%
  with(wordcloud(word_stem, n, max.words = 100, colors = palette_light()))

Instead of looking for the most common words, we can also look for the most common ngrams: here, for the most common word pairs (bigrams) in my followers’ descriptions.

tidy_descr_ngrams <- followers_df %>%
  unnest_tokens(bigram, description, token = "ngrams", n = 2) %>%
  filter(!grepl("\\.|http", bigram)) %>%
  separate(bigram, c("word1", "word2"), sep = " ") %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word)

bigram_counts <- tidy_descr_ngrams %>%
  count(word1, word2, sort = TRUE)
bigram_counts %>%
  filter(n > 10) %>%
  ggplot(aes(x = reorder(word1, -n), y = reorder(word2, -n), fill = n)) +
    geom_tile(alpha = 0.8, color = "white") +
    scale_fill_gradientn(colours = c(palette_light()[[1]], palette_light()[[2]])) +
    coord_flip() +
    theme_tq() +
    theme(legend.position = "right") +
    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
    labs(x = "first word in pair",
         y = "second word in pair")

These, we can also show as a graph:

library(igraph)
library(ggraph)
bigram_graph <- bigram_counts %>%
  filter(n > 5) %>%
  graph_from_data_frame()

set.seed(1)

a <- grid::arrow(type = "closed", length = unit(.15, "inches"))
ggraph(bigram_graph, layout = "fr") +
  geom_edge_link(aes(edge_alpha = n), show.legend = FALSE,
                 arrow = a, end_cap = circle(.07, 'inches')) +
  geom_node_point(color =  palette_light()[1], size = 5, alpha = 0.8) +
  geom_node_text(aes(label = name), vjust = 1, hjust = 0.5) +
  theme_void()

We can also use bigram analysis to identify negated meanings (this will become relevant for sentiment analysis later). So, let’s look at which words are preceded by “not” or “no”.

bigrams_separated <- followers_df %>%
  unnest_tokens(bigram, description, token = "ngrams", n = 2) %>%
  filter(!grepl("\\.|http", bigram)) %>%
  separate(bigram, c("word1", "word2"), sep = " ") %>%
  filter(word1 == "not" | word1 == "no") %>%
  filter(!word2 %in% stop_words$word)

not_words <- bigrams_separated %>%
  filter(word1 == "not") %>%
  inner_join(get_sentiments("afinn"), by = c(word2 = "word")) %>%
  count(word2, score, sort = TRUE) %>%
  ungroup()
not_words %>%
  mutate(contribution = n * score) %>%
  arrange(desc(abs(contribution))) %>%
  head(20) %>%
  mutate(word2 = reorder(word2, contribution)) %>%
  ggplot(aes(word2, n * score, fill = n * score > 0)) +
    geom_col(show.legend = FALSE) +
    scale_fill_manual(values = palette_light()) +
    labs(x = "",
         y = "Sentiment score * number of occurrences",
         title = "Words preceded by \"not\"") +
    coord_flip() +
    theme_tq()

What’s the predominant sentiment in my followers’ descriptions?

For sentiment analysis, I will exclude the words with a negated meaning from nrc and switch their positive and negative meanings from bing (although in this case, there was only one negated word, “endorsement”, so it won’t make a real difference).

tidy_descr_sentiment <- tidy_descr %>%
  left_join(select(bigrams_separated, word1, word2), by = c("word" = "word2")) %>%
  inner_join(get_sentiments("nrc"), by = "word") %>%
  inner_join(get_sentiments("bing"), by = "word") %>%
  rename(nrc = sentiment.x, bing = sentiment.y) %>%
  mutate(nrc = ifelse(!is.na(word1), NA, nrc),
         bing = ifelse(!is.na(word1) & bing == "positive", "negative", 
                       ifelse(!is.na(word1) & bing == "negative", "positive", bing)))
tidy_descr_sentiment %>%
  filter(nrc != "positive") %>%
  filter(nrc != "negative") %>%
  gather(x, y, nrc, bing) %>%
  count(x, y, sort = TRUE) %>%
  filter(n > 10) %>%
  ggplot(aes(x = reorder(y, n), y = n)) +
    facet_wrap(~ x, scales = "free") +
    geom_col(color = palette_light()[1], fill = palette_light()[1], alpha = 0.8) +
    coord_flip() +
    theme_tq() +
    labs(x = "",
         y = "count of sentiment in followers' descriptions")

Are followers’ descriptions mostly positive or negative?

The majority of my followers have predominantly positive descriptions.

tidy_descr_sentiment %>%
  count(screenName, word, bing) %>%
  group_by(screenName, bing) %>%
  summarise(sum = sum(n)) %>%
  spread(bing, sum, fill = 0) %>%
  mutate(sentiment = positive - negative) %>%
  ggplot(aes(x = sentiment)) +
    geom_density(color = palette_light()[1], fill = palette_light()[1], alpha = 0.8) +
    theme_tq()

What are the most common positive and negative words in followers’ descriptions?

library(reshape2)
tidy_descr_sentiment %>%
  count(word, bing, sort = TRUE) %>%
  acast(word ~ bing, value.var = "n", fill = 0) %>%
  comparison.cloud(colors = palette_light()[1:2],
                   max.words = 100)

Topic modeling: are there groups of followers with specific interests?

Topic modeling can be used to categorize words into groups. Here, we can use it to see whether (some) of my followers can be grouped into subgroups according to their descriptions.

library(topicmodels)
dtm_words_count <- tidy_descr %>%
  mutate(word_stem = removeNumbers(word_stem)) %>%
  count(screenName, word_stem, sort = TRUE) %>%
  ungroup() %>%
  filter(word_stem != "") %>%
  cast_dtm(screenName, word_stem, n)

# set a seed so that the output of the model is predictable
dtm_lda <- LDA(dtm_words_count, k = 5, control = list(seed = 1234))

topics_beta <- tidy(dtm_lda, matrix = "beta")
p1 <- topics_beta %>%
  filter(grepl("[a-z]+", term)) %>% # some words are Chinese, etc. I don't want these because ggplot doesn't plot them correctly
  group_by(topic) %>%
  top_n(10, beta) %>%
  ungroup() %>%
  arrange(topic, -beta) %>%
  mutate(term = reorder(term, beta)) %>%
  ggplot(aes(term, beta, color = factor(topic), fill = factor(topic))) +
    geom_col(show.legend = FALSE, alpha = 0.8) +
    scale_color_manual(values = palette_light()) +
    scale_fill_manual(values = palette_light()) +
    facet_wrap(~ topic, ncol = 5) +
    coord_flip() +
    theme_tq() +
    labs(x = "",
         y = "beta (~ occurrence in topics 1-5)",
         title = "The top 10 most characteristic words describe topic categories.")
user_topic <- tidy(dtm_lda, matrix = "gamma") %>%
  arrange(desc(gamma)) %>%
  group_by(document) %>%
  top_n(1, gamma)
p2 <- user_topic %>%
  group_by(topic) %>%
  top_n(10, gamma) %>%
  ggplot(aes(x = reorder(document, -gamma), y = gamma, color = factor(topic))) +
    facet_wrap(~ topic, scales = "free", ncol = 5) +
    geom_point(show.legend = FALSE, size = 4, alpha = 0.8) +
    scale_color_manual(values = palette_light()) +
    scale_fill_manual(values = palette_light()) +
    theme_tq() +
    coord_flip() +
    labs(x = "",
         y = "gamma\n(~ affiliation with topics 1-5)")
library(grid)
library(gridExtra)
grid.arrange(p1, p2, ncol = 1, heights = c(0.7, 0.3))

The upper of the two plots above show the words that were most strongly grouped to five topics. The lower plots show my followers with the strongest affiliation with these five topics.

Because in my tweets I only cover a relatively narrow range of topics (i.e. related to data), my followers are not very diverse in terms of their descriptions and the five topics are not very distinct.

If you find yourself in any of the topics, let me know if you agree with the topic that was modeled for you!

For more text analysis, see my post about text mining and sentiment analysis of a Stuff You Should Know Podcast.