Applying text analyses methods on 382 COVID19 journal articles

Data Science

This blog post is a continuation of my previous blog post on applications of text mining on sampled COVI19 publications.

Mihiretu Kebede(PhD)
2020-08-11

Load necessary packages

For this analysis, I need the following packages

library(dplyr) #for data management
library(ggplot2) #for plotting
library(tidytext) #for text mining
library(bib2df) #for converting bib file to data frame
library(wordcloud) #for plotting most frequent words

Data

Let’s import our bibliographic data using bib2df package as described in my previous blog post

library(bib2df)
covid19 <- bib2df("covid19.bib")

As usual, let’s have a quick glimpse of our data.

glimpse(covid19)
Rows: 601
Columns: 32
$ CATEGORY     <chr> "MISC", "ARTICLE", "ARTICLE", "ARTICLE", "MISC"~
$ BIBTEXKEY    <chr> "Gelzinis2020", "Coccia2020", "Ataguba2020", "S~
$ ADDRESS      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
$ ANNOTE       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
$ AUTHOR       <list> "Gelzinis, Theresa A", "Coccia, Mario", <"Atag~
$ BOOKTITLE    <chr> "Journal of cardiothoracic and vascular anesthe~
$ CHAPTER      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
$ CROSSREF     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
$ EDITION      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
$ EDITOR       <list> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
$ HOWPUBLISHED <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
$ INSTITUTION  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
$ JOURNAL      <chr> NA, "The Science of the total environment", "Gl~
$ KEY          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
$ MONTH        <chr> "sep", "aug", "dec", "sep", "dec", "nov", "sep"~
$ NOTE         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
$ NUMBER       <chr> "9", NA, "1", NA, "1", NA, NA, "1", NA, NA, "3"~
$ ORGANIZATION <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
$ PAGES        <chr> "2328--2330", "138474", "1788263", "312--321", ~
$ PUBLISHER    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
$ SCHOOL       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
$ SERIES       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
$ TITLE        <chr> "Thoracic Anesthesia in the Coronavirus Disease~
$ TYPE         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
$ VOLUME       <chr> "34", "729", "13", "117", "9", "110", "83", "9"~
$ YEAR         <dbl> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020,~
$ DOI          <chr> "10.1053/j.jvca.2020.05.008", "10.1016/j.scitot~
$ ISSN         <chr> "1532-8422 (Electronic)", "1879-1026 (Electroni~
$ LANGUAGE     <chr> "eng", "eng", "eng", "eng", "eng", "eng", "eng"~
$ PMID         <chr> "32406428", "32498152", "32657669", "32546875",~
$ ABSTRACT     <chr> NA, "This study has two goals. The first is to ~
$ KEYWORDS     <chr> NA, "Air Pollutants,Air Pollution,Betacoronavir~
dim(covid19) #601 documents and 32 variables 
[1] 601  32

Once you read the file, it will be saved in our enviroment as data frame and we can do our analysis. As usual, the first part of the analysis is doing some descriptives and visualizing the data. We will then move to tokenization steps. Token is meaningful unit of unit of a text. This meanigful unit is represented by a word or a term. It is the unit that text analysts are interested in doing their analzsis. The process of breaking down a text to set of tokens is known as tokenization. For example

Text: “COVID19 is the biggest global health crisis of the modern world”. In this text, the tokens are each word. In the tokenization process, each of these words are written in a separate columns(variable) as follows.

In tokenization process, the text documents is changed to tokens and each token is counted.

Text COVID19 is the biggest global health crisis of the modern world
COVID19 is the biggest global health crisis of the modern world 1 1 1 1 1 1 1 1 1 1 1

In tokenization process, the text documents is changed to tokens and each token is counted. Notice “the” is mentioned twice. So, we can count it and reshuffle our table as fllows.

Text COVID19 is the biggest global health crisis of modern world
COVID19 is the biggest global health crisis of the modern world 1 1 2 1 1 1 1 1 1 1

The next thing after tokenization is removing irrelevant words. These are words that don’t give much information to the concept of the document other than making the grammatical structure of the document. The stp_words data has about 1150 irrelevant words. These words can be easily removed using the dyplyr ant_join function.

We have enough of theory. Let’s move to the more technical things. As we did in the previous blog, we will only need journal articiles having no missing data on their abstract variable.

library(dplyr)
covid19new <- covid19 %>%
  filter(!is.na(ABSTRACT)) #remove all records with missing abstracts
table(covid19new$CATEGORY) #382 are journal articles with no missing abstract

ARTICLE    MISC 
    382      60 
# Filter journal articles having  abstracts 
covid19new <- covid19new %>% 
  filter(CATEGORY=="ARTICLE") 
#Check 
table(covid19new$CATEGORY) #382 journal articles

ARTICLE 
    382

Tokenization

# select few variables from our data set
data <- covid19new %>% 
  select("TITLE", "ABSTRACT", "KEYWORDS", "AUTHOR")

To start with the tokenization process, we will load stop_words data. We can also add our own stop stop words. In text mining, words on both sides of extreme frequency are no relevant. These words can be removed by creating a customized list of stop_words. Since we are doing our text analysis on journal articles, words that are obviously common in each of the abstracts can be removed. Structured abstracts usually contain “Introduction” or “background”, “Methods”, “Results” and “conclusions”. These and other words can be removed. The custom stop words can also include “covid”, since all of them are about “covid”. There is also a way to remove numbers from our corpus. For now, we will not remove all numbers.

data(stopwords)
custom_stop_words <- bind_rows(tibble(word=c("covid", "covid19","covid-19", "sars", "sars", "cov", "background", "introduction","aims", "objectives", "materials", "methods", "results", "conclusions","textless", "0", "1","2", "3", "4", "5", "6", "7", "8", "9","19", "2019", "2020", "95"),
                                      lexicon = c("custom")),
                               stop_words)


tidy_covid_data <- data %>% 
  unnest_tokens(input=ABSTRACT, output=word) %>% 
  anti_join(stop_words) %>% 
  anti_join(custom_stop_words)

dim(tidy_covid_data) # 40669 tokens, 4 variables
[1] 40647     4

count number of words and visualize it using barchart.

 tidy_covid_data %>% 
  count(word, sort=T) %>% 
   filter(n>100) %>% 
   mutate(word=reorder(word, n)) %>% 
   ggplot(aes(x=word, y=n)) + 
   geom_col(fill="#619CFF") + coord_flip()

Visualize most frequent words using wordcloud

library(wordcloud)
pal <- brewer.pal(8, "Dark2")
tidy_covid_data %>% 
  count(word) %>% 
  with(wordcloud(word, n, max.words=700, colors = pal))

Relationship between words

The relationship between words can be vizualized in a netork graphs after tokenizing using n-grams. n-grams is breaking a text in two-, three or n-number of word tokens.

For now, we will only see bigrams(two-word tokens). Two word tokens can be represented as follows

  1. text1: “How dangerous is COVID19?”

  2. text 2: “How dangerous is COVID19 pandemic.?”

Text how dangerous dangerous is is covi19 covid19 pandemic
How dangerous is COVID19? 1 1 1 0
How dangerous is COVID19 pandemic 1 1 1 1

As we did previously, lets’s do the tokenization once again again. This time usigng bigrams.

covid_bigram <- data[, c(1,2,3)] %>% #selct only titles, abstracts and keywords column of the data. Title is saved
  unnest_tokens(output=bigram, 
                input=ABSTRACT,
                token="ngrams", n=2)

dim(covid_bigram) # 79404 bigrams, 3 variables
[1] 79404     3

Let’s see the most frequent bigrams. Notice the irrelvant words.

head(
  covid_bigram %>%
  count(bigram, sort = TRUE)
)
# A tibble: 6 x 2
  bigram       n
  <chr>    <int>
1 covid 19   989
2 of the     571
3 sars cov   443
4 cov 2      401
5 in the     384
6 of covid   212

Remove useless words

library(tidyr)

covid_bigrams_separated <- covid_bigram %>%
  separate(bigram, c("word1", "word2"), sep = " ")

covd_bigrams_filtered <- covid_bigrams_separated %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word) %>% 
  filter(!word1 %in% custom_stop_words$word) %>%
  filter(!word2 %in% custom_stop_words$word)

dim(covd_bigrams_filtered) # 18981 bigrams, 4 variables
[1] 18963     4
covd_bigrams_filtered_counts <- covd_bigrams_filtered %>% 
  count(word1, word2, sort = TRUE)

head(covd_bigrams_filtered_counts)
# A tibble: 6 x 3
  word1       word2           n
  <chr>       <chr>       <int>
1 coronavirus disease        95
2 acute       respiratory    78
3 respiratory syndrome       75
4 severe      acute          68
5 syndrome    coronavirus    54
6 public      health         49

As you can see from the dim( ) call, the tokenization process changed the abstract of the 382 documents to 41000 tokens. That means we have 382 observations and 41000 one word columns. In the bigram tokenization, we have about 19000 bigrams for our 382 abstracts.

Now, let’s join the separated bigrams.

covid_bigrams_united <- covd_bigrams_filtered %>%
  unite(bigram, word1, word2, sep = " ")

covd_bigrams_joined_counts <- covid_bigrams_united %>% 
  count(bigram, sort = TRUE)

head(covd_bigrams_joined_counts)
# A tibble: 6 x 2
  bigram                   n
  <chr>                <int>
1 coronavirus disease     95
2 acute respiratory       78
3 respiratory syndrome    75
4 severe acute            68
5 syndrome coronavirus    54
6 public health           49

Vizualize bigrams

Word cloud

#Let's visualize 150 most common words
library(wordcloud)
pal <- brewer.pal(8, "Dark2")
covid_bigrams_united %>% 
  count(bigram) %>% 
  with(wordcloud(bigram, n, max.words=150, colors = pal))

The word cloud above shows, “severe acute”, “wuhan china”, “air pollution”, “rt pcr”, “personal protective”, “cytokine storm” and “social distancing” as most common bigrams in the covid19 publications included in this analysis.

Network graph

library(igraph)
library(ggplot2)
bigram_graph <- covd_bigrams_filtered_counts %>%
  filter(n > 8) %>% #words mentioned more than 8 timse
  graph_from_data_frame()

bigram_graph
IGRAPH 6534d4c DN-- 103 70 -- 
+ attr: name (v/c), n (e/n)
+ edges from 6534d4c (vertex names):
 [1] coronavirus->disease      acute      ->respiratory 
 [3] respiratory->syndrome     severe     ->acute       
 [5] syndrome   ->coronavirus  public     ->health      
 [7] health     ->care         nâ         ->â           
 [9] mental     ->health       air        ->quality     
[11] rt         ->pcr          â          ->â           
[13] world      ->health       health     ->organization
[15] pm         ->10           air        ->pollution   
+ ... omitted several edges
library(ggraph)
set.seed(2017)

ggraph(bigram_graph, layout = "fr") +
  geom_edge_link() +
  geom_node_point() +
  geom_node_text(aes(label = name), vjust = 1, hjust = 1)

Term frequency inverse document frequency (tf-idf) is a weighted numerical representation of how a certain word is important in a document. It is calculated using the following formula. tf_idf can be done for the one word columns. For now, let’s just do for bigrams instead.

\[tfidf( t, d, D ) = tf( t, d ) \times idf( t, D )\] \[idf( t, D ) = log \frac{ \text{| } D \text{ |} }{ 1 + \text{| } \{ d \in D : t \in d \} \text{ |} }\]

Where t is the terms appearing in a document; d denotes each document; D denotes the collection of documents.

covid_bigram_tf_idf <- covid_bigrams_united %>%
  count(TITLE, bigram) %>%
  bind_tf_idf(bigram, TITLE, n) %>%
  arrange(desc(tf_idf))

head(covid_bigram_tf_idf)
# A tibble: 6 x 6
  TITLE                                bigram     n    tf   idf tf_idf
  <chr>                                <chr>  <int> <dbl> <dbl>  <dbl>
1 Arthroplasty during the COVID-19 Pa~ arthr~     1 0.5    5.95   2.97
2 Arthroplasty during the COVID-19 Pa~ safel~     1 0.5    5.95   2.97
3 COVID-19 Reveals Brugada Pattern in~ bruga~     3 0.375  5.95   2.23
4 Neutrophils and COVID-19: Nots, NET~ incre~     1 0.333  5.95   1.98
5 Neutrophils and COVID-19: Nots, NET~ pande~     1 0.333  5.25   1.75
6 COVID-19 Time Capsule 1: DATE: Marc~ time ~     2 0.286  5.95   1.70
covid_bigram_tf_idf %>%
  arrange(desc(tf_idf)) %>%
  mutate(bigram = factor(bigram, levels = rev(unique(bigram)))) %>% 
  top_n(15) %>% 
  ggplot(aes(bigram, tf_idf)) +
  geom_col(show.legend = FALSE, fill="cyan4") +
  labs(x = NULL, y = "tf-idf") +
  coord_flip()

The above tf_idf plot shows, “athroplasyty practice”, “safely navigate”, “brugada pattern”, etc were bigrams having the highest tf_idf values.

That is all for today.

Contact

@MihiretuKebede1

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Citation

For attribution, please cite this work as

Kebede(PhD) (2020, Aug. 11). Aspire Data Solutions: Applying text analyses methods on 382 COVID19 journal articles. Retrieved from http://www.mihiretukebede.com/posts/2020-08-10-2020-08-10-text-analysis-of-covid-publications/

BibTeX citation

@misc{kebede(phd)2020applying,
  author = {Kebede(PhD), Mihiretu},
  title = {Aspire Data Solutions: Applying text analyses methods on 382 COVID19 journal articles},
  url = {http://www.mihiretukebede.com/posts/2020-08-10-2020-08-10-text-analysis-of-covid-publications/},
  year = {2020}
}