The function conceptualStructure creates a conceptual structure map of
a scientific field performing Correspondence Analysis (CA), Multiple Correspondence Analysis (MCA) or Metric Multidimensional Scaling (MDS) and Clustering
of a bipartite network of terms extracted from keyword, title or abstract fields.
conceptualStructure(
M,
field = "ID",
ngrams = 1,
method = "MCA",
quali.supp = NULL,
quanti.supp = NULL,
minDegree = 2,
clust = "auto",
k.max = 5,
stemming = FALSE,
labelsize = 10,
documents = 2,
graph = TRUE,
remove.terms = NULL,
synonyms = NULL
)It is an object of the class list containing the following components:
| net | bipartite network | |
| res | Results of CA, MCA or MDS method | |
| km.res | Results of cluster analysis | |
| graph_terms | Conceptual structure map (class "ggplot2") | |
| graph_documents_Contrib | Factorial map of the documents with the highest contributes (class "ggplot2") | |
| graph_docuemnts_TC | Factorial map of the most cited documents (class "ggplot2") |
is a data frame obtained by the converting function
convert2df. It is a data matrix with cases corresponding to
articles and variables to Field Tag in the original ISI or SCOPUS file.
is a character object. It indicates one of the field tags of the standard ISI WoS Field Tag codify. field can be equal to one of these tags:
ID | Keywords Plus associated by ISI or SCOPUS database | |
DE | Author's keywords | |
ID_TM | Keywords Plus stemmed through the Porter's stemming algorithm | |
DE_TM | Author's Keywords stemmed through the Porter's stemming algorithm | |
TI | Terms extracted from titles | |
AB | Terms extracted from abstracts |
is an integer between 1 and 3. It indicates the type of n-gram to extract from texts.
An n-gram is a contiguous sequence of n terms. The function can extract n-grams composed by 1, 2, 3 or 4 terms. Default value is ngrams=1.
is a character object. It indicates the factorial method used to create the factorial map. Use method="CA" for Correspondence Analysis,
method="MCA" for Multiple Correspondence Analysis or method="MDS" for Metric Multidimensional Scaling. The default is method="MCA"
is a vector indicating the indexes of the categorical supplementary variables. It is used only for CA and MCA.
is a vector indicating the indexes of the quantitative supplementary variables. It is used only for CA and MCA.
is an integer. It indicates the minimum occurrences of terms to analyze and plot. The default value is 2.
is an integer or a character. If clust="auto", the number of cluster is chosen automatically, otherwise clust can be an integer between 2 and 8.
is an integer. It indicates the maximum number of cluster to keep. The default value is 5. The max value is 20.
is logical. If TRUE the Porter's Stemming algorithm is applied to all extracted terms. The default is stemming = FALSE.
is an integer. It indicates the label size in the plot. Default is labelsize=10
is an integer. It indicates the number of documents per cluster to plot in the factorial map. The default value is 2. It is used only for CA and MCA.
is logical. If TRUE the function plots the maps otherwise they are saved in the output object. Default value is TRUE
is a character vector. It contains a list of additional terms to delete from the documents before term extraction. The default is remove.terms = NULL.
is a character vector. Each element contains a list of synonyms, separated by ";", that will be merged into a single term (the first word contained in the vector element). The default is synonyms = NULL.
termExtraction to extract terms from a textual field (abstract, title,
author's keywords, etc.) of a bibliographic data frame.
biblioNetwork to compute a bibliographic network.
cocMatrix to compute a co-occurrence matrix.
biblioAnalysis to perform a bibliometric analysis.
# EXAMPLE Conceptual Structure using Keywords Plus
data(scientometrics, package = "bibliometrixData")
CS <- conceptualStructure(scientometrics, field="ID", method="CA",
stemming=FALSE, minDegree=3, k.max = 5)
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