Wasserstein Index Generation model

Wasserstein Index Generation (WIG) model for time-series sentiment index autogeneration

Usage

wig(.data, date_col, docs_col, ...)

# S3 method for class 'data.frame'
wig(.data, date_col, docs_col, specs = wig_specs(), verbose = TRUE, ...)

# S3 method for class 'wig'
print(x, topic = 1, token_per_topic = 10, ...)

# S3 method for class 'wig'
summary(object, topic = 1, token_per_topic = 10, ...)

Arguments

.data

a dataframe containing the dates/datetimes and documents

date_col

name of the column for dates/datetimes

docs_col

name of the column for the texts/documents

...

only for compatibility

specs

list, model specification for WIG see wig_specs for reference

verbose

bool, whether to print useful info

x

WIG model

topic

int, number of topic to be printed

token_per_topic

int, number of tokens to be printed

object

WIG model

Details

This is the re-implementation of WIG model from scratch in R.

References

Xie, F. (2020). Wasserstein index generation model: Automatic generation of time-series index with application to economic policy uncertainty. Economics Letters, 186, 108874. https://doi.org/10.1016/j.econlet.2019.108874

See also

vignette("wdl-model")

Examples

# create a small dataset
wigdf <- data.frame(
  ref_date = as.Date(c("2012-01-01", "2012-02-01")),
  docs = c("this is a sentence", "this is another sentence"))

wigfit <- wig(wigdf, ref_date, docs,
  specs = wig_specs(wdl_control = list(num_topics = 2),word2vec_control = list(min_count = 1)),
  verbose = FALSE)