Corpus clustering based on the Reinert method - Double clustering
Corpus clustering based on the Reinert method - Double clustering
rainette2( x, y = NULL, max_k = 5, uc_size1 = 10, uc_size2 = 15, min_members = 10, min_chi2 = 3.84, ... )
x |
either a quanteda dfm object or the result of |
y |
if |
max_k |
maximum number of clusters to compute |
uc_size1 |
if |
uc_size2 |
if |
min_members |
minimum members of each cluster |
min_chi2 |
minimum chi2 for each cluster |
... |
if |
You can pass a quanteda dfm as x object, the function then performs two simple
clustering with varying minimum uc size, and then proceed to find optimal partitions
based on the results of both clusterings.
If both clusterings have already been computed, you can pass them as x and y arguments
and the function will only look for optimal partitions.
For more details on optimal partitions search algorithm, please see package vignettes.
A tibble with optimal partitions found for each available value of k as rows, and the following
columns :
clusters list of the crossed original clusters used in the partition
k the number of clusters
chi2 sum of the chi2 value of each cluster
n sum of the size of each cluster
groups group membership of each document for this partition (NA if not assigned)
Reinert M, Une méthode de classification descendante hiérarchique : application à l'analyse lexicale par contexte, Cahiers de l'analyse des données, Volume 8, Numéro 2, 1983. http://www.numdam.org/item/?id=CAD_1983__8_2_187_0
Reinert M., Alceste une méthodologie d'analyse des données textuelles et une application: Aurelia De Gerard De Nerval, Bulletin de Méthodologie Sociologique, Volume 26, Numéro 1, 1990. doi: 10.1177/075910639002600103
require(quanteda)
mini_corpus <- head(data_corpus_inaugural, n = 2)
mini_corpus <- split_segments(mini_corpus, 5)
dtm <- dfm(mini_corpus, remove = stopwords("en"), tolower = TRUE, remove_punct = TRUE)
dtm <- dfm_wordstem(dtm, language = "english")
dtm <- dfm_trim(dtm, min_termfreq = 3)
res1 <- rainette(dtm, k = 5, min_uc_size = 2, min_split_members = 2)
res2 <- rainette(dtm, k = 5, min_uc_size = 3, min_split_members = 2)
res <- rainette2(res1, res2, min_members = 2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.