Purpose
The purpose of the package polmineR is to offer a toolset
for the interactive analysis of corpora using R. Apart from performance
and usability, key considerations for developing the package are:
To provide a library with standard tasks such as
concordances/keyword-in-context, cooccurrence statistics, feature
extraction, or preparation of term-document-matrics.
To keep the original text accessible and to offer a seamless
integration of qualitative and quantitative steps in corpus analysis
that facilitates validation.
To make the creation and analysis of subcorpora (‘partitions’) as
easy as possible. A particular strength of the package is to support
research on synchronic and diachronic change of language use.
To offer performance for users with a standard infrastructure.
The package is developed based on the idea of a three-tier software
design. Corpus data are managed and indexed using the Corpus Workbench (CWB), which
serves as a backend.
To support sharing consolidated and documented data, following
the ideas of reproducible research.
The polmineR package supplements R packages that are already
widely used for text mining. The CRAN
NLP task view is a good place to learn about relevant packages. The
polmineR package is intended to be an interface between the Corpus Workbench (CWB), an
efficient system for storing and querying large corpora, and existing
packages for text mining and text statistics.
Apart from the speed of text processing, the Corpus Query Processor
(CQP) and the CQP syntax
provide a powerful and widely used syntax to query corpora. This is not
an unique idea. Using a combination of R and the CWB implies a software
architecture you will also find in the TXM project, or with CQPweb. The
polmineR package offers a library with the grammer of corpus
analysis below a graphical user interface (GUI). It is a toolset to
perform simple tasts efficiently as well as to implement complex
workflows.
Advanced users will benefit from acquiring a good understanding of
the Corpus Workbench. The Corpus
Encoding Tutorial is an authoritative text for that. The vignette
of the rcqp package, albeit archived, includes an excellent
explanation of the CWB data-model. The inferface used now to use CWB/CQP
functionality is the RcppCWB
package.
A basic issue to understand is the difference between s- and
p-attributes. The CWB distinguishes structural attributes
(s-attributes) that will contain the metainformation that can be used to
generate subcorpora/partitions, and positional attributes
(p-attributes). Typically, the p-attributes will be ‘word’, ‘pos’ (for
part-of-speech) and ‘lemma’ (for the lemmatized word form).
Getting started
Loading polmineR
The polmineR package is loaded just like any other
package.
Upon loading the package, the package version is reported. As
polmineR is under active development, please check whether a
more recent version is available at CRAN. Development versions are
available at GitHub.
In addition, you will see an information on the session
registry, which needs some further explanation.
The session registry directory
Indexed corpus data may be stored at different locations on your
machine. CWB users will usually have a data directory with
subdirectories for every single corpus. But corpus data may also reside
within R packages, or anywhere else.
It is not necessary to move indexed corpora to one single location.
The only recommendation is to have them on a device that can be accessed
sufficiently fast. Corpora are not fully loaded into memory, but
information is retrieved from disk on a ‘on demand’-basis. Thus, storing
corpus data on a SSD may be faster than a hard drive.
The CWB will look up information on the corpora in a directory called
registry that is defined by the environment variable
CORPUS_REGISTRY. Starting with version v0.7.9, the polmineR
package creates a temporary registry directory in the temporary session
directory. To get the path of the session registry directory, call
registry(). The output is the session registry you have
seen when loading polmineR.
## /var/folders/fw/qwt11pjx1qs83dl2jwltcvmr0000gn/T/RtmpXUkqK1/polmineR_registry
The session registry directory combines the registry files describing
the corpora polmineR knows about. Upon loading
polmineR, the files in the registry directory defined by the
environment variable CORPUS_REGISTRY are copied to the session registry
directory. To see whether the environment variable CORPUS_REGISTRY is
set, use the Sys.getenv()-function.
Sys.getenv("CORPUS_REGISTRY")
See the annex for an explanation how to set the CORPUS_REGISTRY
environment variable for the current R session, or permanently.
Using and installing packaged corpora
If you want to use a corpus wrapped into a R data package, call
use() with the name of the R package. The function will add
the registry files describing the corpora in the package to the session
registry directory introduced before.
In the followings examples, the REUTERS corpus included in the
polmineR package will be used for demonstration purposes. It is
a sample of Reuters articles that is included in the tm package (cp. http://www.daviddlewis.com/resources/testcollections/reuters21578/),
and may already be known to some R users.
use("polmineR")
use("RcppCWB", corpus = "REUTERS")
Checking corpora are available
The corpus()-method can be used to check which corpora
are described in the registry and accessible. The REUTERS corpus in our
case (note that the names of CWB corpora are always written upper case).
In addition to the English REUTERS corpus, a small subset of the
GermaParl corpus (“GERMAPARLMINI”) is included in the polmineR
package.
## corpus
## 1 AAZ
## 2 ALBB
## 3 ALLZ
## 4 BADZ
## 5 BEZE
## 6 BEZG
## 7 BGM
## 8 BKR
## 9 BKU
## 10 BMP
## 11 BNA
## 12 BR
## 13 BRZE
## 14 BUER
## 15 BVH
## 16 BZ
## 17 COBU
## 18 DAW
## 19 DAZ
## 20 DECH
## 21 DIKI
## 22 DNN
## 23 EXPR
## 24 EZE
## 25 FEPR
## 26 FNP
## 27 FRT
## 28 GAZ
## 29 GERMAPARL2
## 30 GERMAPARLMINI
## 31 GEZE
## 32 GIAN
## 33 GTB
## 34 HA
## 35 HAKU
## 36 HATA
## 37 HAZ
## 38 HIAZ
## 39 HK
## 40 HNA
## 41 HOFZ
## 42 HOZG
## 43 HST
## 44 IDZL
## 45 KIRZ
## 46 KN
## 47 KR
## 48 KRAN
## 49 KSTA
## 50 LAAN
## 51 LAZ
## 52 LAZE
## 53 LBN
## 54 LIN
## 55 LR
## 56 LVZ
## 57 MAER
## 58 MASP
## 59 MATK
## 60 MAZ
## 61 MBGA
## 62 MBVS
## 63 MIB
## 64 MOPO
## 65 MPW
## 66 MUAZ
## 67 MUME
## 68 MUVB
## 69 MUZE
## 70 MZ
## 71 NADIRAFAZ
## 72 NADIRASZ
## 73 NADIRATAZ
## 74 NAZT
## 75 NPHA
## 76 PARTYPAGES
## 77 PARTYPAGES2
## 78 PRIG
## 79 REUTERS
## registry
## 1 /Users/andreasblatte/Data/cwb/registry
## 2 /Users/andreasblatte/Data/cwb/registry
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## 27 /Users/andreasblatte/Data/cwb/registry
## 28 /Users/andreasblatte/Data/cwb/registry
## 29 /Users/andreasblatte/Data/cwb/registry
## 30 /var/folders/fw/qwt11pjx1qs83dl2jwltcvmr0000gn/T/RtmpXUkqK1/polmineR_registry
## 31 /Users/andreasblatte/Data/cwb/registry
## 32 /Users/andreasblatte/Data/cwb/registry
## 33 /Users/andreasblatte/Data/cwb/registry
## 34 /Users/andreasblatte/Data/cwb/registry
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## 78 /Users/andreasblatte/Data/cwb/registry
## 79 /var/folders/fw/qwt11pjx1qs83dl2jwltcvmr0000gn/T/RtmpXUkqK1/polmineR_registry
## encoding type template size
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Session settings
Many methods in the polmineR package use default settings
that are set in the general options settings. Following a convention,
settings relevant for the polmineR package simplystart with
‘polmineR.’ Inspect the settings as follows:
options()[grep("polmineR", names(options()))]
Several methods (such as kwic, or cooccurrences) will use these
settings, if no explicit other value is provided. You can see this in
the usage section of help pages (?kwic, for instance). To
change settings, this is how.
options("polmineR.left" = 5)
options("polmineR.right" = 5)
options("polmineR.mc" = FALSE)
Working with corpora: Core methods
Core analytical tasks are implemented as methods (S4 class system),
i.e. the bevaviour of the methods changes depending on the object that
is supplied. Almost all methods can be applied to corpora (indicated by
a length-one character vector) as well as partitions (subcorpora). As a
quick start, methods applied to corpora are explained first.
Keyword-in-context (kwic)
The kwic method applied to the name of a corpus will return a KWIC
object. Output will be shown in the viewer pane of RStudio. Technically,
a htmlwidget is prepared which offers some convenient functionality.
k <- kwic("REUTERS", "oil")
You can include metadata from the corpus into the kwic display using
the ‘s_attributes’ argument. Let us start with one s-attribute.
k <- kwic("REUTERS", "oil", s_attributes = "places")
But you can display any number of s-attributes.
k <- kwic("REUTERS", "oil", s_attributes = c("id", "places"))
You can also use the CQP query syntax for formulating queries. That
way, you can find multi-word expressions, or match in a manner you may
know from using regular expressions.
k <- kwic("REUTERS", '"oil" "price.*"')
Explaining the CQP syntax goes beyon this vignette. Consult the CQP
tutorial to learn more about the CQP syntax.
Getting counts and frequencies
You can count one or several hits in a corpus.
cnt <- count("REUTERS", "Kuwait")
cnt <- count("REUTERS", c("Kuwait", "USA", "Bahrain"))
cnt <- count("REUTERS", c('"United" "States"', '"Saudi" "Arabia.*"'), cqp = TRUE)
Dispersions
Use the dispersion()-method to get dispersions of counts
accross one (or two) dimensions.
oil <- dispersion("REUTERS", query = "oil", s_attribute = "id", progress = FALSE)
saudi_arabia <- dispersion(
"REUTERS", query = '"Saudi" "Arabia.*"',
s_attribute = "id", cqp = TRUE, progress = FALSE
)
Note that it is a data.table that is returned. You can proceed to a
visualisation easily.
barplot(height = saudi_arabia[["count"]], names.arg = saudi_arabia[["id"]], las = 2)
Cooccurrences
To analyse the neighborhood of a token, or the match for a CQP query,
use cooccurrences().
oil <- cooccurrences("REUTERS", query = "oil")
sa <- cooccurrences("REUTERS", query = '"Saudi" "Arabia.*"', left = 10, right = 10)
top5 <- subset(oil, rank_ll <= 5)
In an interactive session, simply type top5 in the
terminal, and the output will be shown in the data viewer. To inspect
the output in the viewer pane, you can coerce the object to a
htmlwidget. This is also a good way how to include the table in a
Rmarkdown document.
For further operations, get the the table with the statistical
results by applying the as.data.frame()-method.
## word word_id count_partition count_coi count_ref exp_coi exp_ref
## 1 prices 12 47 27 20 9.229607 37.770393
## 2 crude 14 20 13 7 3.927492 16.072508
## 3 industry 78 10 8 2 1.963746 8.036254
## 4 recent 356 6 5 1 1.178248 4.821752
## 5 on 169 17 8 9 3.338369 13.661631
## ll rank_ll
## 1 33.045786 1
## 2 19.616220 2
## 3 16.968527 3
## 4 11.331186 4
## 5 6.505618 5
Working with subcorpora - partitions
Working with partitions (i.e. subcorpora) based on s-attributes is an
important feature of the ‘polmineR’ package. So if we want to work with
the articles in the REUTERS corpus related to Kuaweit in 2006:
kuwait <- partition("REUTERS", places = "kuwait", regex = TRUE)
To get some basic information about the partition that has been set
up, the ‘show’-method can be used. It is also called when you simply
type the name of the partition object.
## ** partition object **
## corpus: REUTERS
## name:
## s-attributes: places = kuwait
## cpos: 3 pairs of corpus positions
## size: 660 tokens
## count: not available
To evaluate s-attributes, regular expressions can be used.
saudi_arabia <- partition("REUTERS", places = "saudi-arabia", regex = TRUE)
s_attributes(saudi_arabia, "id")
## [1] "242" "248" "273" "349" "352"
If you work with a flat XML structure, the order of the provided
s-attributes may be relevant for speeding up the set up of the
partition. For a nested XML, it is important that with the order, you
move from ancestors to childs. For further information, see the
documentation of the partition-function.
Cooccurrences
The cooccurrences-method can be applied to partition-objects.
saudi_arabia <- partition("REUTERS", places = "saudi-arabia", regex = TRUE)
oil <- cooccurrences(saudi_arabia, "oil", p_attribute = "word", left = 10, right = 10)
Note that is is possible to provide a query that uses the full CQP
syntax. The statistical analysis of collocations to the query can be
accessed as the slot “stat” of the context object. Alternatively, you
can get the table with the statistics using
as.data.frame.
df <- as.data.frame(oil)
df[1:5, c("word", "ll", "rank_ll")]
## word ll rank_ll
## 1 prices 5.383314 1
## 2 by 5.081723 2
## 3 Gulf 4.944806 3
## 4 crude 2.968707 4
## 5 last 2.756452 5
Distribution of queries
To understand the occurance of a phenomenon, the distribution of
query results across one or two dimensions will often be interesing.
This is done via the ‘distribution’ function. The query may use the CQP
syntax.
q1 <- dispersion(saudi_arabia, query = 'oil', s_attribute = "id", progress = FALSE)
q2 <- dispersion(saudi_arabia, query = c("oil", "barrel"), s_attribute = "id", progress = FALSE)
Getting features
To identify the specific vocabulary of a corpus of interest, a
statistical test based (chi square, or log likelihood) can be
performed.
saudi_arabia <- partition("REUTERS", places = "saudi-arabia", regex = TRUE)
saudi_arabia <- enrich(saudi_arabia, p_attribute = "word")
saudi_arabia_features <- features(saudi_arabia, "REUTERS", included = TRUE)
saudi_arabia_features_min <- subset(saudi_arabia_features, rank_chisquare <= 10.83 & count_coi >= 5)
saudi_arabia_features_min
To extract the statistical information, you can also use the
as.data.frame-method.
df <- as.data.frame(saudi_arabia_features_min)
df_min <- df[,c("word", "count_coi", "count_ref", "chisquare")]
Getting a tm TermDocumentMatrix
For many applications, term-document matrices are the point of
departure. The tm class TermDocumentMatrix serves as an input to several
R packages implementing advanced text mining techniques. Obtaining this
input from a corpus imported to the CWB will usually involve setting up
a partitionBundle and then applying a method to get the matrix.
articles <- corpus("REUTERS") %>% partition_bundle(s_attribute = "id", progress = FALSE)
## ℹ s-attribute "id" has values: yes
## ℹ get regions and get values
## ✔ get regions and get values [5ms]
##
## ℹ instantiate objects (n = 20)
## ✔ instantiate objects (n = 20) [16ms]
##
## ℹ assign names
## ✔ assign names [3ms]
##
articles_count <- count(articles, p_attribute = "word")
tdm <- as.TermDocumentMatrix(articles_count, col = "count", verbose = FALSE)
class(tdm) # to see what it is
## [1] "TermDocumentMatrix" "simple_triplet_matrix"
## <<TermDocumentMatrix (terms: 1192, documents: 20)>>
## Non-/sparse entries: 2409/21431
## Sparsity : 90%
## Maximal term length: 15
## Weighting : term frequency (tf)
m <- as.matrix(tdm) # turn it into an ordinary matrix
m[c("oil", "barrel"),]
## Docs
## Terms 127 144 191 194 211 236 237 242 246 248 273 349 352 353 368 489 502
## oil 5 12 2 1 0 6 4 2 5 6 5 4 4 4 3 4 5
## barrel 2 0 1 1 0 3 0 0 0 2 2 0 1 0 0 1 1
## Docs
## Terms 543 704 708
## oil 2 3 1
## barrel 1 0 0
Reading
A key consideration of the polmineR package is to offer tools for
combining quantitative and qualitative approaches to text analysis. Use
the html()-method, or the read()-method to
return to the full text. In this example, we define a maximum height for
the output, which is useful when including full text output in a
Rmarkdown document.
P <- partition("REUTERS", id = "248")
H <- html(P, height = "250px")
H
Corpus: REUTERS
Saudi Arabian Oil Minister Hisham Nazer reiterated the kingdom’s commitment to last December’s OPEC accord to boost world oil prices and stabilise the market the official Saudi Press Agency SPA said Asked by the agency about the recent fall in free market oil prices Nazer said Saudi Arabia is fully adhering by the Accord and it will never sell its oil at prices below the pronounced prices under any circumstance Nazer quoted by SPA said recent pressure on free market prices may be because of the end of the northern hemisphere winter season and the glut in the market Saudi Arabia was a main architect of the December accord under which OPEC agreed to lower its total output ceiling by 7.25 pct to 15.8 mln barrels per day bpd and return to fixed prices of around 18 dlrs a barrel The agreement followed a year of turmoil on oil markets which saw prices slump briefly to under 10 dlrs a barrel in mid 1986 from about 30 dlrs in late 1985 Free market prices are currently just over 16 dlrs Nazer was quoted by the SPA as saying Saudi Arabia’s adherence to the accord was shown clearly in the oil market He said contacts among members of OPEC showed they all wanted to stick to the accord In Jamaica OPEC President Rilwanu Lukman who is also Nigerian Oil Minister said the group planned to stick with the pricing agreement We are aware of the negative forces trying to manipulate the operations of the market but we are satisfied that the fundamentals exist for stable market conditions he said Kuwait’s Oil Minister Sheikh Ali al Khalifa al Sabah said in remarks published in the emirate’s daily Al Qabas there were no plans for an emergency OPEC meeting to review prices Traders and analysts in international oil markets estimate OPEC is producing up to one mln bpd above the 15.8 mln ceiling They named Kuwait and the United Arab Emirates along with the much smaller producer Ecuador among those producing above quota Sheikh Ali denied that Kuwait was over producing REUTER