Motivation

Purpose: The focus of the package ‘polmineR’ is the interactive analysis of corpora using R. Core objectives for the development of the package are performance, usability, and a modular design.

Aims: Key aims for developing the package are:

• To keep the original text accessible. A seamless integration of qualitative and quantitative steps in corpus analysis supports validation, based on inspecting the text behind the numbers.

• To provide a library with standard tasks. It is an open source platform that will make text mining more productive, avoiding prohibitive costs to reimplement basics, or to run many lines of code to perform a basic tasks.

• To create a package that makes the creation and analysis of subcorpora (‘partitions’) easy. A particular strength of the package is to support contrastive/comparative research.

• To offer performance for users with a standard infrastructure. The package picks up the idea of a three-tier software design. Corpus data are managed and indexed by using the Open Corpus Workbench (CWB). The CWB is particularly efficient for storing large corpora and offers a powerful language for querying corpora, the Corpus Query Processor (CQP).

• To support sharing consolidated and documented data, following the ideas of reproducible research.

Background: The polmineR-package was specifically developed to make full use of the XML annotation structure of the corpora created in the PolMine project (see polmine.sowi.uni-due.de). The core PolMine corpora are corpora of plenary protocols. In these corpora, speakers, parties etc. are structurally annotated. The polmineR-package is meant to help making full use of the rich annotation structure.

Core Functions

Upon loading polmineR, a message will report the version of the package and the location of a so-called ‘registry’-directory.

library(polmineR)

The session registry directory is populated with files that describe the corpora that are present and accessible on the user’s system.

Install and use packaged corpora

Indexed sample corpora wrapped into R data packages can be installed from the drat-repository of the PolMine Project.

The GermaParl package includes only a small excerpt the GermaParl corpus for demo purposes, the europarl package does not contain data at all. Yet the packages include functionality to download the full corpora.

if (!"GermaParl" %in% rownames(installed.packages())){
install.packages("GermaParl", repos = "http://polmine.github.io/drat")
}
use("GermaParl")
#> ... activating corpus: GERMAPARLMINI
if (!"GERMAPARL" %in% corpus()$corpus){ GermaParl::germaparl_download_corpus() use("GermaParl") } if (!"europarl" %in% rownames(installed.packages())){ install.packages("europarl", repos = "http://polmine.github.io/drat") } use("europarl") #> ... activating corpus: EUROPARL-DE #> ... activating corpus: EUROPARL-EN #> ... activating corpus: EUROPARL-ES #> ... activating corpus: EUROPARL-FR #> ... activating corpus: EUROPARL-IT #> ... activating corpus: EUROPARL-NL if (!"EUROPARL-EN" %in% corpus()$corpus){
use("europarl")
}

Calling the use()-function will activate a corpus included in a data package. The registry files describing the corpora in a package are added to the session registry directory.

An advantage of keeping corpora in data packages are the versioning and documentation mechanisms that are the hallmark of packages. Of course, polmineR will work with the library of CWB indexed corpora stored on your machine. The corpora described in the registry directory defined by the environment variable CORPUS_REGISTRY will be added to the session registry directory when loading polmineR.

partition (and partition_bundle)

All methods can be applied to a whole corpus, as well as to partitions (i.e. subcorpora). Use the metadata of a corpus (so-called s-attributes) to define a subcorpus.

ep2005 <- partition("EUROPARL-EN", text_year = "2006")
#> ... get encoding: latin1
#> ... get cpos and strucs
size(ep2005)
#> [1] 3100529
barroso <- partition("EUROPARL-EN", speaker_name = "Barroso", regex = TRUE)
#> ... get encoding: latin1
#> ... get cpos and strucs
size(barroso)
#> [1] 98142

Partitions can be bundled into partition_bundle objects, and most methods can be applied to a whole corpus, a partition, or a partition_bundle object alike. Consult the package vignette to learn more.

count (using CQP syntax)

Counting occurrences of a feature in a corpus, a partition or in the partitions of a partition_bundle is a basic operation. By offering access to the query syntax of the Corpus Query Processor (CQP), polmineR package exposes a query syntax that goes far beyond regular expressions. See the CQP documentation to learn more.

count("EUROPARL-EN", "France")
#>     query count         freq
#> 1: France  5517 0.0001399122
count("EUROPARL-EN", c("France", "Germany", "Britain", "Spain", "Italy", "Denmark", "Poland"))
#>      query count         freq
#> 1:  France  5517 1.399122e-04
#> 2: Germany  4196 1.064114e-04
#> 3: Britain  1708 4.331523e-05
#> 4:   Spain  3378 8.566676e-05
#> 5:   Italy  3209 8.138089e-05
#> 6: Denmark  1615 4.095673e-05
#> 7:  Poland  1820 4.615557e-05
count("EUROPARL-EN", '"[pP]opulism"')
#>            query count         freq
#> 1: "[pP]opulism"   107 2.713542e-06

dispersion (across one or two dimensions)

The dispersion method is there to analyse the dispersion of a query, or a set of queries across one or two dimensions (absolute and relative frequencies). The CQP syntax can be used.

populism <- dispersion("EUROPARL-EN", "populism", s_attribute = "text_year", progress = FALSE)
pop_regex <- dispersion("EUROPARL-EN", '"[pP]opulism"', s_attribute = "text_year", cqp = TRUE, progress = FALSE)

cooccurrences (to analyse collocations)

The cooccurrences method is used to analyse the context of a query (including some statistics).

islam <- cooccurrences("EUROPARL-EN", query = 'Islam', left = 10, right = 10)
islam <- subset(islam, rank_ll <= 100)
dotplot(islam)

features (keyword extraction)

Compare partitions to identify features / keywords (using statistical tests such as chi square).

ep_2002 <- partition("EUROPARL-EN", text_year = "2002", p_attribute = "word")
ep_pre_2002 <- partition("EUROPARL-EN", text_year = 1997:2001, p_attribute = "word")
features(ep_2002, ep_pre_2002, included = FALSE) %>%
subset(rank_chisquare <= 10) %>%
format() %>%
knitr::kable(format = "markdown")
rank_chisquare word count_coi count_ref exp_coi chisquare
1 2002 1694 782 398.96 5011.70
2 Johannesburg 479 21 80.57 2348.97
3 Seville 378 26 65.10 1792.96
4 Barcelona 706 528 198.84 1542.16
5 ’s 10694 36727 7641.03 1457.07
6 2003 549 329 141.47 1399.45
7 Copenhagen 575 430 161.94 1256.06
8 terrorism 1221 1917 505.63 1206.67
9 02 233 2 37.87 1198.75
10 candidate 1217 2088 532.54 1048.84

kwic (also known as concordances)

So what happens in the context of a word, or a CQP query? To attain valid research results, reading will often be necessary. The kwic method will help, and uses the conveniences of DataTables, outputted in the Viewer pane of RStudio.

kwic("EUROPARL-EN", "Islam", meta = c("text_date", "speaker_name")) %>%
as.data.frame() %>%
.[1:8,] %>%
knitr::kable(format = "markdown", escape = FALSE)
meta left node right
1996-05-09
Oostlander
, as for example with Islam here in Europe , so
1996-05-09
Féret
promotion of the study of Islam in Europe ’ , with
1996-05-09
Féret
seem to have forgotten that Islam makes no distinction between spiritual
1996-06-05
von Habsburg
, the old arguments against Islam are trotted out time and
1996-06-05
von Habsburg
various shades of opinion within Islam must not simply be lumped
1996-06-05
von Habsburg
there are various groups within Islam and that many of them
1996-07-17
Blot
represented by the growth of Islam to the south and east
1996-09-18
Stirbois
rushing into the arms of Islam . A fortnight later ,

Corpus analysis involves moving from text to numbers, and back again. Use the read method, to inspect the full text of a partition (a speech given by chancellor Angela Merkel in this case).

use("GermaParl")
merkel <- partition("GERMAPARL", speaker = "Angela Merkel", date = "2013-09-03")
read(merkel)

as.TermDocumentMatrix (for text mining purposes)

Many advanced methods in text mining require term document matrices as input. Based on the metadata of a corpus, these data structures can be obtained in a fast and flexible manner, for performing topic modelling, machine learning etc.

use("europarl")
speakers <- partition_bundle(
"EUROPARL-EN", s_attribute = "speaker_id",
progress = FALSE, verbose = FALSE
)
speakers_count <- count(speakers, p_attribute = "word", progress = TRUE)
tdm <- as.TermDocumentMatrix(speakers_count, col = "count")
dim(tdm)

Installation

Windows

The following instructions assume that you have installed R. If not, install it fromCRAN. An installation of RStudio is highly recommended.

The CRAN release of polmineR can be installed using install.packages(), all dependencies will be installed, too.

install.packages("polmineR")

To install the most recent development version that is hosted in a GitHub repository, use the installation mechanism offered by the devtools package.

install.packages("devtools")
devtools::install_github("PolMine/polmineR", ref = "dev")

Check the installation by loading polmineR and activating the corpora included in the package.

library(polmineR)
corpus()

MacOS

The following instructions for Mac users assume that R is installed on your system. Binaries are available from the Homepage of the R Project. An installation of RStudio is highly recommended. Get the Open Source License version of RStudio Desktop.

At this stage, the RcppCWB dependency is not available as a pre-compiled binary and needs to be compiled. A set of system requirements needs to be fulfilled to do this.

First, you will need an installation of Xcode, which you can get it via the Mac App Store. You will also need the Command Line Tools for Xcode. It can be installed from a terminal with:

xcode-select --install

Second, an installation of XQuartz is required. It can be obtained from www.xquartz.org.

Third, to fulfill the system requirements of the RcppCWB package, the Glib and pcre libraries need to be installed. Using a package manager makes things considerably easier. We recommend using ‘Homebrew’. To install Homebrew, follow the instructions on the Homebrew Homepage. The following commands then need to be executed from a terminal window. They will install the C libraries that the RcppCWB package relies on:

brew -v install pkg-config
brew -v install glib --universal
brew -v install pcre --universal
brew -v install readline

The latest release of polmineR can be installed from CRAN using the usual install.packages-function.

install.packages("polmineR")

The development version of polmineR can be installed using devtools:

install.packages("devtools") # unless devtools is already installed
devtools::install_github("PolMine/polmineR", ref = "dev")

Check whether everything works by loading polmineR, and activating the demo corpora included in the package.

library(polmineR)
use("polmineR")
corpus()

Linux (Ubuntu)

If you have not yet installed R on your Ubuntu machine, there is a good instruction at ubuntuuser. To install base R, enter in the terminal.

sudo apt-get install r-base r-recommended

Make sure that you have installed the latest version of R. The following commands will add the R repository to the package sources and run an update. The second line assumes that you are using Ubuntu 16.04.

sudo apt-key adv --recv-keys --keyserver keyserver.ubuntu.com E084DAB9
sudo apt-get update
sudo apt-get upgrade

It is highly recommended to install RStudio, a powerful IDE for R. Output of polmineR methods is generally optimized to be displayed using RStudio facilities. If you are working on a remote server, running RStudio Server may be an interesting option to consider.

The RcppCWB package, the interface used by polmineR to query CWB corpora, will require the pcre, glib and pkg-config libraries. They can be installed as follows. In addition libxml2 is installed, a dependency of the R package xml2 that is used for manipulating html output.

sudo apt-get install libglib2.0-dev libssl-dev libcurl4-openssl-dev
sudo apt-get install libxml2-dev
sudo apt-get install libprotobuf-dev

The system requirements will now be fulfilled. From R, install dependencies for rcqp/polmineR first, and then rcqp and polmineR.

install.packages("RcppCWB")
install.packages("polmineR")

Use devtools to install the development version of polmineR from GitHub.

install.packages("devtools")
devtools::install_github("PolMine/polmineR", ref = "dev")

You may want to install packaged corpora to run examples in the vignette, and the man packages.

library(polmineR)
use("polmineR")
corpus()

To have access to all package functions and to run all package tests, the installation of further system requirements and packages is required. The xlsx dependency requires that rJava is installed and configured for R. That is done on the shell:

sudo apt-get install openjdk-8-jre
sudo R CMD javareconf

To run package tests including (re-)building the manual and vignettes, a working installation of Latex is required, too. Be aware that this may be a time-consuming operation.

sudo apt-get install texlive-full texlive-xetex 

Now install the remaining packages from within R.

install.packages(pkgs = c("rJava", "xlsx", "tidytext"))

Quoting polmineR

The polmineR package has been developed to be useful for research. If you publish research results making use of polmineR, the following citation is suggested to be included in publications.

Blaette, Andreas (2020). polmineR: Verbs and Nouns for Corpus Analysis. R package version v0.8.5. http://doi.org/10.5281/zenodo.4042093