get_terms: get_terms function

In TextForecast: Regression Analysis and Forecasting Using Textual Data from a Time-Varying Dictionary

get_terms function

Description

get_terms function

Usage

get_terms(
  corpus_dates,
  ntrms_words,
  st,
  path.name,
  ntrms_collocation,
  ngrams_number,
  min_freq,
  language
)

Arguments

corpus_dates	a character vector indicating the subfolders where the texts are located.
ntrms_words	maximum numbers of words that will be filtered by tf-idf. We rank the word by tf-idf in a decreasing order. Then, we select the words with the ntrms highest tf-idf.
st	set 0 to stem the words and 1 otherwise.
path.name	the folders path where the subfolders with the dates are located.
ntrms_collocation	maximum numbers of collocations that will be filtered by tf-idf. We rank the collocations by tf-idf in a decreasing order. Then, after we select the words with the ntrms highest tf-idf.
ngrams_number	integer indicating the size of the collocations. Defaults to 2, indicating to compute bigrams. If set to 3, will find collocations of bigrams and trigrams.
min_freq	integer indicating the frequency of how many times a collocation should at least occur in the data in order to be returned.
language	the texts language. Default is english.

Value

a list containing a sparse matrix with the all collocations and words couting and another with a tf-idf filtered collocations and words counting according to the ntrms.

Examples

st_year=2017
end_year=2018
path_name=system.file("news",package="TextForecast")
#qt=paste0(sort(rep(seq(from=st_year,to=end_year,by=1),12)),
#c("m1","m2","m3","m4","m5","m6","m7","m8","m9","m10","m11","m12"))
#z_terms=get_terms(corpus_dates=qt[1:23],path.name=path_name,
#ntrms_words=500,ngrams_number=3,st=0,ntrms_collocation=500,min_freq=10)
#
path_name=system.file("news",package="TextForecast")
days=c("2019-30-01","2019-31-01")
z_terms=get_terms(corpus_dates=days[1],path.name=path_name,
ntrms_words=500,ngrams_number=3,st=0,ntrms_collocation=500,min_freq=1)

TextForecast documentation built on April 25, 2022, 9:06 a.m.