Advancing Text Mining with R and quanteda

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Everyone is talking about text analysis. Is it puzzling that this data source is so popular right now? Actually no. Most of our datasets rely on (hand-coded) textual information. Extracting, processing, and analyzing this oasis of information becomes increasingly relevant for a large variety of research fields. This Methods Bites Tutorial by Cosima Meyer summarizes Cornelius Puschmann’s workshop in the MZES Social Science Data Lab in January 2019 on advancing text mining with R and the package quanteda. The workshop offered guidance through the use of quanteda and covered various classification methods, including classification with known categories (dictionaries and supervised machine learning) and with unknown categories (unsupervised machine learning).

What is quanteda?

In order to analyze text data, R has several packages available. In this blog post we focus on quanteda. quanteda is one of the most popular R packages for the quantitative analysis of textual data that is fully-featured and allows the user to easily perform natural language processing tasks. It was originally developed by Ken Benoit and other contributors. It offers an extensive documentation and is regularly updated. quanteda is most useful for preparing data that can then be further analyzed using unsupervised/supervised machine learning or other techniques. A combination with tidyverse leads to a more transparent code structure and offers a mere variety of useful areas that could not be addressed within the limited time of the workshop (e.g., scaling models, part-of-speech (POS) tagging, named entities, word embeddings, etc.).

There are also similar R packages such as tm, tidytext, and koRpus. tm has simpler grammer but slightly fewer features, tidytext is very closely integrated with dplyr and well-documented, and koRpus is good for tasks such as part-of-speech (POS) tagging).

How do we use quanteda?

Most analyses in quanteda require three steps:

1. Import the data

The data that we usually use for text analysis is available in text formats (e.g., .txt or .csv files).

2. Build a corpus

After reading in the data, we need to generate a corpus. A corpus is a type of dataset that is used in text analysis. It contains “a collection of text or speech material that has been brought together according to a certain set of predetermined criteria” (Shmelova et al. 2019, p. 33). These criteria are usually set by the researchers and are in concordance with the guiding question. For instance, if you are interested in analyzing speeches in the UN General Debate, these predetermined criteria are the time and scope conditions of these debates (speeches by countries at different points in time).

3. Calculate a data frequency matrix (DFM)

Another essential component for text analysis is a data frequency matrix (DFM); also called document-term matrix (DTM). These two terms are synonyms but quanteda refers to a DFM whereas others will refer to DTM. It describes how frequently terms occur in the corpus by counting single terms.


To generate a DFM, we first split the text into its single terms (tokens). We then count how frequently each term (token) occurs in each document.

The following graphic describes visually how we turn raw text into a vector-space representation that is easily accessible and analyzable with quantitative statistical tools. It also visualizes how we can think of a DFM. The rows represent the documents that are part of the corpus and the columns show the different terms (tokens). The values in the cells indicate how frequently these terms (tokens) are used across the documents.



Model of a DFM

Figure 1: Model of a DFM

Important things to remember about DFMs:

  • A corpus is positional (string of words) and a DFM is non-positional (bag of words). Put differently, the order of the words matters in a corpus whereas a DFM does not have information on the position of words.
  • A token is each individual word in a text (but it could also be a sentence, paragraph, or character). This is why we call creating a “bag of words” also tokenizing text. In a nutshell, a DFM is a very efficient way of organizing the frequency of features/tokens but does not contain any information on their position. In our example, the features of a text are represented by the columns of a DFM and aggregate the frequency of each token.
  • In most projects you want one corpus to contain all your data and generate many DFMs from that.
  • The rows of a DFM can contain any unit on which you can aggregate documents. In the example above, we used the single documents as the unit. It may also well be more fine-grained with sub-documents or more aggregated with a larger collection of documents.
  • The columns of a DFM are any unit on which you can aggregate features. Features are extracted from the texts and quantitatively measurable. Features can be words, parts of the text, content categories, word counts, etc. In the example above, we used single words such as “united”, “nations”, and “peace”.

To showcase the three steps introduced above, we are using the UN General Debate data by Mikhaylov, Baturo, and Dasandi dataset. There is also a pre-processed version of the dataset accessible with quanteda.corpora.

How to access the UNGD data with quanteda.corpora

# Install package quanteda.corpora
devtools::install_github("quanteda/quanteda.corpora")

# Load the dataset
quanteda.corpora::data_corpus_ungd2017

We will, however, mainly rely on the original dataset throughout the following explanations to match closely the regular workflow of textual data in R. If you want to replicate the steps, please download the data here and unzip the zip file. Your global_path should direct you to the text file folders.

In a first step, we need to load the necessary packages and read in the data.

# Load all required packages
library(tidyverse)        # Also loads dplyr, ggplot2, and haven
library(quanteda)         # For NLP
library(readtext)         # To read .txt files
library(stm)              # For structural topic models
library(stminsights)      # For visual exploration of STM
library(wordcloud)        # To generate wordclouds
library(gsl)              # Required for the topicmodels package
library(topicmodels)      # For topicmodels
library(caret)            # For machine learning

# Download data here: 
# https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/0TJX8Y 
# and unzip the zip file

# Read in data (.txt files)
global_path 

We can then proceed and generate a corpus.

mycorpus 

As we can see (by calling the object mycorpus), the corpus consists of 8,093 documents.

Output: mycorpus

mycorpus
Corpus consisting of 8,093 documents and 4 docvars.

With this data, we can already generate first descriptive statistics.

# Save statistics in "mycorpus.stats"
mycorpus.stats 
##               Text Types Tokens Sentences country session year Textno
## 1  ALB_25_1970.txt  1728   9078       256     ALB      25 1970     01
## 2  ARG_25_1970.txt  1425   5192       218     ARG      25 1970     02
## 3  AUS_25_1970.txt  1612   5690       270     AUS      25 1970     03
## 4  AUT_25_1970.txt  1340   4717       164     AUT      25 1970     04
## 5  BEL_25_1970.txt  1288   4786       207     BEL      25 1970     05
## 6  BLR_25_1970.txt  1427   6138       204     BLR      25 1970     06
## 7  BOL_25_1970.txt  1560   5613       225     BOL      25 1970     07
## 8  BRA_25_1970.txt  1333   4427       154     BRA      25 1970     08
## 9  CAN_25_1970.txt   728   1887        97     CAN      25 1970     09
## 10 CMR_25_1970.txt   928   3144       106     CMR      25 1970     10

In a next step, we can also calculate the data frequency matrix. To do so, first we need to generate tokens (tokens()) and can also already pre-process the data. This includes removing the numbers (remove_numbers), punctuations (remove_punct), symbols (remove_symbols), twitter characters such as @ and # (remove_twitter), urls beginning with http(s) (remove_url), and hyphens (remove_hyphens). We further include the docvars from our corpus (include_docvars).

# Preprocess the text

# Create tokens
token 

Since the pre-1994 documents were scanned with OCR scanners, several tokens with combinations of digits and characters were introduced. We clean them manually following this guideline.

# Clean tokens created by OCR
token_ungd 

In the next step, we then create the data frequency matrix. We lower and stem the words (tolower and stem) and remove common stop words (remove=stopwords()). Stopwords are words that appear in texts but do not give the text a substantial meaning (e.g., “the”, “a”, or “for”). Since the language of all documents is English, we only remove English stopwords here. quanteda can also deal with stopwords from other languages (for more information see here).

mydfm 

We can also trim the text with dfm_trim. Using the command and its respective specifications, we filter words that appear less than 7.5% and more than 90%. This rather conservative approach is possible because we have a sufficiently large corpus.

mydfm.trim 

To get a look at the DFM, we now print their first 10 observations and first 10 features:

# And print the results of the first 10 observations and first 10 features in a DFM
head(dfm_sort(mydfm.trim, decreasing = TRUE, margin = "both"),
     n = 10,
     nf = 10) 
Document-feature matrix of: 10 documents, 10 features (6.0% sparse).
10 x 10 sparse Matrix of class "dfm"
                 features
docs              problem session conflict council africa global resolut hope south situat
  CUB_34_1979.txt      36       8        1       0     13      3      10    8    10     23
  IRL_39_1984.txt      41       9       18      11     14      5      16   21    19      9
  PAN_37_1982.txt      14      12       12       8     11      2       6   11    20     10
  BFA_29_1974.txt      25      17        1       4     15      0      10   20     6      9
  GRC_43_1988.txt      27       9       13      14     10      2      10   10    11      5
  PRY_38_1983.txt      30      17       12       3      0      3      21   10     8     16
  RUS_31_1976.txt      16      12        4       2      8      0      16    2     6      9
  UGA_30_1975.txt      26      17        2       7     54      0       9    9    12      8
  RUS_32_1977.txt      13       8       12       1     12      0      10    3     6     11
  ALB_28_1973.txt      17       4        6       1      5      1       3    2     9     13

The sparsity gives us information about the proportion of cells that have zero counts.

Classification

A next step can involve the classification of the text. The article by Grimmer and Stewart (2013) provides a good overview for this step. The upcoming section follows their structure. Classification sorts texts into categories. The following picture is leaned on the figure by Grimmer and Stewart (2013, 268) and illustrates a possible structure of classification.

A researcher usually faces one of the following situations: The categories are known beforehand or the categories are unknown. If the researcher knows the categories, s/he can use automated methods to minimize the workload that is associated with the categorization of the texts. Throughout the workshop, two methods were presented: a dictionary method and a supervised method. If the researcher does not know the categories, s/he is likely to resort to unsupervised machine learning. The following section provides illustrative examples for both methods.

Known categories

Known categories: Dictionaries

Dictionaries contain lists of words that correspond to different categories. If we apply a dictionary approach, we count how often words that are associated with different categories are represented in each document. These dictionaries help us to classify (or categorize) the speeches based on the frequency of the words that they contain. Popular dictionaries are sentiment dictionaries (such as Bing, Afinn or LIWC) or LexiCoder.

We use the “LexiCoder Policy Agenda” dictionary that can be accessed here in a .lcd format.


The “LexiCoder Policy Agenda” dictionary captures major topics from the comparative Policy Agenda project and is currently available in Dutch and English.

To read in the dictionary, we use quanteda’s built-in function dictionary().

# load the dictionary with quanteda's built-in function
dict 

We apply this dictionary to filter the share of each country’s speeches on immigration, international affair and defence.

mydfm.un %
  dplyr::rename(country = document) %>%
  select(country, immigration, intl_affairs, defence) %>%
  tidyr::gather(immigration:defence, key = "Topic", value = "Share") %>%
  group_by(country) %>%
  mutate(Share = Share / sum(Share)) %>%
  mutate(Topic = haven::as_factor(Topic))

In a next step, we can visualize the results with ggplot. This gives us a first impression of the distribution of the topics in the 2018 UN General Debate across countries.

un.topics.pa %>%
  ggplot(aes(country, Share, colour = Topic, fill = Topic)) +
  geom_bar(stat = "identity") +
  scale_colour_brewer(palette = "Set1") +
  scale_fill_brewer(palette = "Pastel1") +
  ggtitle("Distribution of PA topics in the UN General Debate corpus") +
  xlab("") +
  ylab("Topic share (%)") +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank())



Distribution of PA topics in the UN General Debate corpus

Figure 3: Distribution of PA topics in the UN General Debate corpus

We observe a relatively high share for both defence and international affairs whereas immigration receives fewer attention in the speeches.

Known categories: Supervised machine learning – Naive Bayes (NB)

We now turn to supervised machine learning. Similar to the dictionary approach explained above, this method also requires some pre-existing classifications. But in contrast to a dictionary, we now divide the data into a training and a test dataset. This follows the general logic of machine learning algorithms. The training data already contains the classifications and trains the algorithm (e.g., our Naive Bayes classifier) to predict the class of our speech based on the features that are given. A Naive Bayes classifier now calculates the probability for each class based on the features. It eventually goes for the class with the highest probability and selects this class as the corresponding category. It is based on the Bayes theorem for conditional probability. It can be formally written as:

[ P(A | B) = frac{P(A) * P(B | A)}{P(B)}]


In plain words, the probability of A is conditional on B.

Bayes’ theorem

  • (A) and (B) are events
  • (P(A)) and (P(B)) is the probability of observing (A) and (B) (respectively) independent from each other
  • (P(A) neq 0) and (P(B) neq 0)
  • (P(A|B)) is the conditional probability that (A) occurs when (B) is true

    [P(A|B) = frac{P(A cap B)}{P(B)}, if P(B) neq 0]
  • (P(B|A)) is the conditional probability that (B) occurs when (A) is true

    [P(B|A) = frac{P(B cap A)}{P(A)}, if P(A) neq 0]
  • And we also have the joint probability of $ P(A B) = P(B A)$ because

[ Longrightarrow P(A cap B) = P(A|B)P(B) = P(B|A)P(A)]

[ Longrightarrow P(A | B) = frac{P(A cap B)}{P(B)}]

[ Longrightarrow P(A | B) = frac{frac{P(A cap B)}{P(B)}*P(A)}{P(B)}]

[ Longrightarrow P(A | B) = frac{P(B|A)*P(A)}{P(B)}]

Why is Naive Bayes “naive”? Naive Bayes is “naive” because of its strong independence assumptions. It assumes that all features are equally important and that all features are independent. If you think of n-grams and compare unigrams and bigrams, you can intuitively understand why the last assumption is a strong assumption. A unigram counts each word as a gram (“I” “like” “walking” “in” “the” “sun”) whereas a bigram counts two words as a gram (“I like” “like walking” “walking in” “in the” “the sun”).

However, even when the assumptions are not fully met, Naive Bayes still performs well.

A Naive Bayes is a relatively simple classification algorithm because it does not require much time and working capacity of your machine. To use a Naive Bayes classifier, we rely on quanteda’s built-in function textmodel_nb.

To perform the Naive Bayes estimation, we proceed with the following steps:

1. We set up training and test data based on the corpus.

2. Based on these two datasets, we generate a DFM.

3. We train the algorithm by feeding in the training data and eventually use the test data for performance.

4. We then check the performance (accuracy) of our results.

5. And compare it with a random prediction.

For this example, we use the pre-labeled dataset that is used for the algorithm newsmap by Kohei Watanabe. The dataset contains information on the geographical location of newspaper articles. We introduce this new dataset as Naive Bayes – a supervised machine learning algorithm – requires pre-labeled data.

We first load the dataset. To do so, we follow Kohei Watanabe’s description here, download the corpus of Yahoo News from 2014, and follow the subsequent processing steps he describes.

# load data
load("../newspaper.RData")

# transform variables
pred_data$text 

For simplicity, we keep only the USA, Great Britain, France, Brazil, and Japan.

pred_data %
  dplyr::filter(country %in% c("us", "gb", "fr", "br", "jp")) %>%
                  dplyr::select(text, country)
head(pred_data)
row text country
1 ’08 French champ Ivanovic loses to Safarova in 3rd. PARIS (AP) – Former French Open champion Ana Ivanovic lost in the third round Saturday, beaten 6-3, 6-3 by 23rd-seeded Lucie Safarova of the Czech Republic. fr
2 Up to USD1,000 a day to care for child migrants. More than 57,000 unaccompanied children, mostly from Central America, have been caught entering the country illegally since last October, and President Barack Obama has asked for USD3.7 billion in emergency funding to address what he has called an ‘urgent humanitarian solution.’ ‘One of the figures that sticks in everybody’s mind is we’re paying about USD250 to USD1,000 per child,’ Senator Jeff Flake told reporters, citing figures presented at a closed-door briefing by Homeland Security Secretary Jeh Johnson. Federal authorities are struggling to find more cost-effective housing, medical care, counseling and legal services for the undocumented minors. The base cost per bed was USD250 per day, including other services, Senator Dianne Feinstein said, without providing details. us
3 1,400 gay weddings in England, Wales in first three months. Just over 1,400 gay couples tied the knot in the three months after same-sex marriage was allowed in England and Wales, figures out Thursday showed. The Office for National Statistics said 1,409 marriages took place between March 29 and June 30. ‘The novelty and significance of marriage becoming available led to an initial rush among same-sex couples wanting to be among the very first to assume the same rights and protection afforded to heterosexual couples,’ said James Brown, a partner at law firm JMW Solicitors. The figures will likely surge from December once civil partnerships can be converted into marriages. gb
4 1 dead after fan fighting in Brazil. SAO PAULO (AP) – Police say a 21-year-old man died after a confrontation between rival football fan groups in Brazil on Sunday. br
5 1 dead as plane with French tourists crashes in US. PAGE, Arizona (AP) – Authorities say a small plane carrying French tourists crashed while trying to land at an airport in Arizona, and one person was killed and another hospitalized. fr
6 1 US theory is someone diverted missing plane. WASHINGTON (AP) – A U.S. official says investigators are examining the possibility that someone caused the disappearance of a Malaysia Airlines jet with 239 people on board, and that it may have been ‘an act of piracy.’ us

We pre-process the data again. Our final corpus thus includes the newspaper headlines by country.

data_corpus 

In a first step, we need to define our training and our test dataset. Based on these two datasets, we generate a DFM. This code is based on Cornelius code and quanteda’s example. To do so, we apply similar general data pre-processing steps as discussed above.

# Set a seed for replication purposes
set.seed(68159)

# Generate random 10,000 numbers without replacement
training_id %
  dfm(stem = TRUE)

# Get test set (documents not in training_id)
dfmat_test %
  dfm(stem = TRUE)

We can now check the distribution of the countries across the two DFMs:

print(prop.table(table(docvars(
  dfmat_training, "country"
))) * 100)
   br    fr    gb    jp    us 
13.34 14.37 33.65 11.10 27.54 
print(prop.table(table(docvars(
  dfmat_test, "country"
))) * 100)
   br       fr       gb       jp       us 
13.70893 15.13151 33.02630 10.93542 27.19783 

As we can see, the countries are equally distributed across both DFMs.

In a next step, we train the Naive Bayes classifier. Going back to the formula stated above, we know that A is conditional on B.

[ P(A | B) = frac{P(A) * P(B | A)}{P(B)}]

A is what we want to know (the country that is mainly addressed in each text) and B is what we see (the text). We can now proceed and replace A and B with the respective terms. This leads us to the next equation:

[ P(Country | Text) = frac{P(Country) * P(Text | Country)}{P(Text)}]

We can then proceed and train our algorithm using quanteda’s built-in function textmodel_nb.

# Train naive Bayes
# The function takes a DFM as the first argument 
model.NB 
dfmat_matched 

The command summary(model.NB) gives us the results of our prediction. Click unfold to see the results.

Code: summary(model.NB)

summary(model.NB)
Call:
textmodel_nb.dfm(x = dfmat_training, y = docvars(dfmat_training, 
    "country"), prior = "docfreq")

Class Priors:
(showing first 5 elements)
    br     fr     gb     jp     us 
0.1334 0.1437 0.3365 0.1110 0.2754 

Estimated Feature Scores:
         '     08   french   champ  ivanov    lose      to safarova     in     3rd      .     pari
br 0.08949 0.1679 0.007791 0.07636 0.08195 0.08707 0.09989   0.1474 0.1214 0.34322 0.1176 0.008863
fr 0.14212 0.2778 0.940061 0.37912 0.20345 0.16469 0.13803   0.3659 0.1473 0.24345 0.1296 0.947706
gb 0.43332 0.1532 0.032008 0.24397 0.14963 0.49395 0.35845   0.1346 0.3540 0.17905 0.3321 0.032363
jp 0.06507 0.1184 0.004396 0.10771 0.28900 0.06580 0.10287   0.1040 0.1007 0.06916 0.1059 0.002778
us 0.27000 0.2827 0.015743 0.19285 0.27598 0.18850 0.30077   0.2482 0.2766 0.16512 0.3148 0.008290
         (      ap       )      -  former    open champion     ana    lost    the   third   round
br 0.16080 0.24091 0.16100 0.1611 0.09034 0.17116  0.12708 0.08142 0.08045 0.1101 0.08964 0.30833
fr 0.14221 0.14820 0.14219 0.1363 0.17312 0.18636  0.24110 0.13475 0.16643 0.1350 0.17178 0.19535
gb 0.34860 0.28303 0.34870 0.3403 0.45720 0.36654  0.51497 0.07433 0.38923 0.3603 0.39194 0.24189
jp 0.09123 0.08125 0.09126 0.0922 0.04583 0.07443  0.05684 0.22969 0.07943 0.1005 0.07653 0.04757
us 0.25715 0.24661 0.25685 0.2702 0.23351 0.20152  0.06002 0.47982 0.28446 0.2940 0.27010 0.20686
   saturday      ,  beaten     6-3      by 23rd-seed
br  0.14287 0.1078 0.19323 0.21226 0.09731    0.1955
fr  0.19921 0.1384 0.20558 0.40985 0.12900    0.3236
gb  0.44845 0.3437 0.45359 0.21530 0.33256    0.1785
jp  0.09248 0.1099 0.07787 0.08317 0.11636    0.1379
us  0.11699 0.3001 0.06972 0.07942 0.32477    0.1646

To better understand how well we did, we can also generate two frequency tables for right and wrong predictions.

prop.table(table(predict(model.NB) == docvars(dfmat_training, "country"))) * 100
FALSE  TRUE 
 2.71 97.29  

To check if this result indicates a good performance, we compare it with a random result. We randomize the list of countries (and keep the overall frequency distribution of our countries constant) to allow our random algorithm a legitimate chance for a correct classification.

prop.table(table(sample(predict(model.NB)) == docvars(dfmat_training, "country"))) * 100
FALSE  TRUE 
76.63 23.37  

As we can see from the the summarized table below, our Naive Bayes classifier clearly outperforms a random algorithm.

Naive Bayes Random
False 2.71 76.63
True 97.29 23.37

We are likely to increase our accuracy even more by pre-processing our text data.

A confusion matrix helps us assess how well our algorithm performed. It shows us the prediction for all five countries in contrast to the actual class that is given by our data. For example, we predict 2580 articles as belonging to Great Britain that actually belong to Great Britain. However, we also predict 39 articles as British articles while they are actually French. Overall, when we look at the diagonal, we see that most predictions correctly classify the articles and that our algorithm performs well.

actual_class 
             predicted_class
actual_class   br   fr   gb   jp   us
          br 2580    6   50    6   37
          fr   39 2697  124   11   86
          gb   24   52 6072   24  282
          jp   33    9   31 1945  119
          us   56   32  194   66 4967

We store our confusion matrix in an object because we need it later to visualize the results.

confusion 
Confusion Matrix and Statistics

            predicted_class
actual_class   br   fr   gb   jp   us
          br 2580    6   50    6   37
          fr   39 2697  124   11   86
          gb   24   52 6072   24  282
          jp   33    9   31 1945  119
          us   56   32  194   66 4967

Overall Statistics
                                          
               Accuracy : 0.9344          
                 95% CI : (0.9309, 0.9379)
    No Information Rate : 0.3311          
    P-Value [Acc > NIR] : 

To display our confusion matrix visually, we could either produce a heatmap or a confusion matrix.

We first plot a heatmap using the code below.

Code: Heatmap using ggplot2

# Save confusion matrix as data frame
confusion.data 



Contingency table

Figure 4: Contingency table

To plot the following confusion matrix, we need to slightly adjust the code from this post on data visualization by Richard Traunmüller.

Code: Confusion matrix

# Generate a data matrix from the confusion table
dat 



Contingency table

Figure 5: Contingency table

Both figures show us that our prediction performs well for all countries but it performs particularly well for the USA and Great Britain. Darker colors show a higher frequency in both plots, the contingency table also indicates a greater frequency with the size of the bubbles.

Unknown categories

Unknown categories: Unsupervised machine learning – Latent semantic analysis (LSA)

The next section addresses how to analyze texts with unknown categories. Latent Semantic Analysis (LSA) evaluates documents and seeks to find the underlying meaning or concept of these documents. If each word only had one meaning, LSA would have an easy job. However, oftentimes, words are ambiguous, have multiple meanings or are synonyms. One example from our corpus is “may” – it could be a verb, a noun for a month, or a name. To overcome this problem, LSA essentially compares how often words appear together in one document and then compares this across all other documents. By grouping words with other words, we try to identify those words that are semantically related and eventually also get the true meaning of ambiguous words.


More technically, LSA is a useful technique for aligning feature distributions to an n-dimensional space. This is achieved via singular value decomposition (SVD). This decomposition allows us to decompose both a quadratic and a rectangular matrix.


LSA can (among other things) be used to compare similarity of documents/documents grouped by some variable.

What are the major assumptions and simplifications that LSA has?

  1. Documents are non-positional (“bag of words”). The “bag of words” approach assumes that the order of the words does not matter. What matters is only the frequency of the single words.
  2. Concepts are understood as patterns of words where certain words often go together in similar documents.
  3. Words only have one meaning given the contexts surrounding the patterns of words.

For the next example, we go back to the UN General Assembly speech data set.

corpus.un.sample 

We again follow the cleaning steps described above.

Code: Data pre-processing steps

# Create tokens
token_sample 

And then run the textmodel_lsa command.

mylsa 

One interesting question would be: How similar are the USA and Russia? Each dot represents a country-year observation. The USA are colored blue, Russia is colored red, and all other countries are grey.

# We need the "stringr" package for the following command
sources 



Distribution of PA topics in the UN General Debate corpus

Figure 6: Distribution of PA topics in the UN General Debate corpus

On Dimension 2 we do not really observe a difference between documents from the US and Russia while we do see a topical divide on Dimension 1.

# create an LSA space; return its truncated representation in the low-rank space
tmod 
# matrix in low_rank LSA space
tmod$matrix_low_rank[, 1:5]
                presid    particular       pleasur        convey        solemn
SYR_25_1970.txt      9  3.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00
LBN_36_1981.txt      8  6.000000e+00  3.574198e-15  4.361095e-15  1.000000e+00
BLZ_61_2006.txt      1  2.319256e-13 -3.872892e-15  1.630640e-15  3.945845e-15
IDN_58_2003.txt      1  4.000000e+00  5.224885e-13  1.000000e+00  4.128642e-15
BOL_62_2007.txt     16  1.000000e+00  9.374921e-13 -1.542308e-13  1.865500e-14
ESP_59_2004.txt      1  1.000000e+00  1.096398e-12  1.000000e+00 -4.602221e-14
BEL_46_1991.txt      3  3.000000e+00  1.059028e-12 -1.910607e-13 -2.341183e-14
MLT_56_2001.txt      2 -2.442491e-14  9.686462e-13  1.000000e+00 -3.266137e-14
NAM_53_1998.txt      6  3.000000e+00  1.407991e-12  3.701761e-13  6.992887e-15
MWI_60_2005.txt      3  1.384795e-13  5.593955e-13 -1.283140e-13 -1.853978e-14

We now fold the queries into the space generated by dfmat[1:10,] and return its truncated versions of its representation in the new low-rank space. For more information on this, see Deerwester et al. (1990) and Rosario (2000).

pred 
4 x 10 Matrix of class "dgeMatrix"
                      [,1]        [,2]      [,3]       [,4]        [,5]       [,6]        [,7]
PAK_46_1991.txt -0.3854266 -0.04484373 0.1220358 -0.1098016  0.13608271 0.20572826  0.06882769
PRY_73_2018.txt -0.2258879 -0.10037933 0.1089027 -0.1457731 -0.01366223 0.06686449 -0.05633359
ITA_37_1982.txt -0.4558678 -0.06585580 0.1772039 -0.1827881  0.11242120 0.13485329 -0.05808548
KAZ_61_2006.txt -0.2305213 -0.05878778 0.1231484 -0.2164697  0.18169974 0.08266246 -0.04646643
                       [,8]        [,9]      [,10]
PAK_46_1991.txt  0.07593185 -0.12207856 -0.0150233
PRY_73_2018.txt -0.08488202 -0.06309237 -0.1158875
ITA_37_1982.txt  0.12460691 -0.06632978 -0.1762249
KAZ_61_2006.txt  0.08702912 -0.07348411 -0.1685055
Unknown categories: Unsupervised machine learning – Latent Dirichlet Allocation (LDA)

Both Latent Dirichlet Allocation (LDA) and Structural Topic Modeling (STM) belong to topic modelling. Topic models find patterns of words that appear together and group them into topics. The researcher decides on the number of topics and the algorithms then discover the main topics of the texts without prior information, training sets or human annotations.

LDA is a Bayesian mixture model for discrete data where topics are assumed to be uncorrelated. It is a model that describes how the documents in a dataset were created. We assign an arbitrary number of topics (K) where each topic is a distribution over a fixed vocabulary. Each document is considered as a collection of words, one for each of K topics. It also follows the “bag of words” approach that considers each word in a document separately.

To calculate the LDA models, we need to load the package topicmodels. If you use this package for the first time on your machine, you need to execute a specific sequence of commands, detailed in the code below.

Code: Installing the package topicmodels

# 1) Install GSL
# We first need to make sure that GSL (e.g. 'brew install gsl' in the terminal) is installed on our machine

# 2) Install gsl
# Then we proceed and choose either of the following commands:
# A)
install.packages("gsl")
# or B)
install.packages(
  "https://cran.rstudio.com/src/contrib/gsl_2.1-6.tar.gz",
  repos = NULL,
  method = "libcurl"
)

# 3) Load the gsl package
library(gsl)

# 4) Install topicmodels
# Here we choose again either of the following commands:
# A)
install.packages("topicmodels")
# or B)
install.packages(
  "https://cran.r-project.org/src/contrib/topicmodels_0.2-8.tar.gz",
  repos = NULL,
  method = "libcurl"
)

# 5) Load the topicmodels package
library(topicmodels)

# Now you are all set for the following models.

For the LDA, we again first trim our DFM. As above, the command dfm_trim trimms the text. This allows us to filter words that appear less than 7.5% and more than 90%.

mydfm.un.trim 

We then assign an arbitrary topic number and convert the trimmed DFM to a topicmodels object.

# Assign an arbitrary number of topics
topic.count 

Eventually, we can calculate the LDA model with quanteda’s LDA() command.

lda.model 

Output for lda.model

lda.model
A LDA_VEM topic model with 15 topics.
as.data.frame(terms(lda.model, 6))
topic1 topic2 topic3 topic4 topic5 topic6 topic7 topic8 topic9 topic10
nuclear cooper problem trade america arab arab global oper war
weapon council interest problem american small palestinian cooper negoti today
republ reform hope product latin nuclear israel social conflict now
soviet global possibl per respect issu territori order problem power
relat effect now economi law pacif isra futur confer live
disarma process even increas principl weapon resolut common south mani
topic11 topic12 topic13 topic14 topic15
global terror african deleg africa
sustain council africa session south
climat terrorist republ problem independ
chang iraq conflict concern african
challeng resolut democrat great namibia
goal law elect republ struggl

How similar are the fifteen topics? This question is particularly interesting because it allows us to (possibly) cluster homogeneous topics. To get a better idea of our LDA model and about the similarity among the different topics, we can plot our results using the following chunck of code. dist and hclust are standard R commands that allow us to calculate the similarity.

lda.similarity [email protected]) %>%
  scale() %>%
  dist(method = "euclidean") %>%
  hclust(method = "ward.D2")

par(mar = c(0, 4, 4, 2))
plot(lda.similarity,
     main = "LDA topic similarity by features",
     xlab = "",
     sub = "")



LDA topic similarity by features

Figure 7: LDA topic similarity by features

The plot is called dendogram and visualizes a hierarchial clustering. The x-axis gives you the topics and the clusters of these topics. Put differently, it gives you information on the smilarity of the topics. On the y-axis, we see the dissmilarity (or distance) between our fifteen topics.

Unknown categories: Unsupervised machine learning – Structural topic models (STM)

The structural topic models (STM) are a popular extension of the standard LDA models. The STM allows to include metadata (the information about each document) into the topicmodel and it offers an alternative initialization mechanism (“Spectral”). For STMs, the covariates can be used in priors. The stm vignette provides a good overview how to use a STM. The package includes estimation algorithms and tools for every stage of the workflow. A particularly large emphasis is on a number of diagnostic functions that are integrated into the R package.

The package stiminsights is very useful for visual exploration. It allows the user to process interactive validation, interpretation and visualization of one or several Structural Topic Models (stm).

We again trim our dfm with the command dfm_trim.

mydfm.un.trim 

We then assign the number of topics arbitrarily.

topic.count 

And eventually convert the DFM (with convert()) and calculate the STM (with stm()).

# Calculate the STM 
dfm2stm 

To get a first insight, we print the terms that appear in each topic.

as.data.frame(t(labelTopics(model.stm, n = 10)$prob))
V1 V2 V3 V4 V5 V6 V7 V8 V9
council lebanon soviet global african nuclear european trade african
reform arab union sustain africa weapon europ economi situat
effect resolut relat challeng republ treati cooper product guinea
activ problem militari chang democrat disarma union industri africa
cooper territori forc climat millennium arm conflict resourc particular
strengthen palestinian republ goal commit test process increas hope
convent withdraw arm commit poverti non stabil market concern
role principl war agenda particular prolifer contribut price solut
confer solut socialist respons like pakistan bosnia global session
contribut war polici address summit india respect financi problem
V10 V11 V12 V13 V14 V15 V16 V17 V18
council problem cooper republ power africa order problem israel
iraq now stabil korea great south principl session arab
resolut oper dialogu democrat viet debt social oper palestinian
law negoti terror china deleg deleg univers confer isra
aggress hope sudan south coloni hope cultur solut palestin
charter agreement call korean territori process today resolut east
violat mani process asia nam environ life cyprus middl
iraqi last promot north republ confer respect concern resolut
islam way commit east sea welcom societi negoti occupi
iran possibl issu cooper charter conflict becom disarma territori
V19 V20 V21 V22 V23 V24 V25
island today per terror america conflict africa
small war cent afghanistan american refuge south
pacif live social afghan latin war independ
caribbean want educ cooper central somalia african
chang terror poverti terrorist respect assist struggl
ocean mani health problem social humanitarian regim
issu let drug global process mani namibia
sea now democraci asia solut elect apartheid
climat everi programm central possibl situat coloni
call know million issu express forc racist

The following plot allows us to intuitively get information on the share of the different topics at the overall corpus.

plot(
  model.stm,
  type = "summary",
  text.cex = 0.5,
  main = "STM topic shares",
  xlab = "Share estimation"
)



STM topic shares

Figure 8: STM topic shares

Using the package stm, we can now visualize the different words of a topic with a wordcloud. Since topic 4 has the highest share, we use it for the next visualization. The location of the words is randomized and changes each time we plot the wordcloud while the size of the words is relative to their frequency and remains the same.

stm::cloud(model.stm,
           topic = 4,
           scale = c(2.25, .5))



Wordcloud with `stm`

Figure 9: Wordcloud with stm

If we want, we can also put several different topics in visually perspective using the following lines of code:

plot(model.stm,
     type = "perspectives",
     topics = c(4, 5),
     main = "Putting two different topics in perspective")



Wordcloud using `stm` -- Perspective plots

Figure 10: Wordcloud using stm – Perspective plots

The perspective plot visualizes the combination of two topics (here topic 4 and topic 5). The size of the words is again relative to their frequency (within the combination of the two topics). The x-axis shows the dregree that specific words align with Topic 4 or Topic 5. Global is closely aligned with Topic 4 whereas commit is more central in both topics.


       



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