All functions

add_features()

Add feature columns to a (sento_)corpus object

aggregate(<sentiment>)

Aggregate textual sentiment across sentences, documents and time

aggregate(<sento_measures>)

Aggregate sentiment measures

as.data.table(<sento_measures>)

Get the sentiment measures

as.sentiment()

Convert a sentiment table to a sentiment object

as.sento_corpus()

Convert a quanteda or tm corpus object into a sento_corpus object

attributions()

Retrieve top-down model sentiment attributions

compute_sentiment()

Compute textual sentiment across features and lexicons

corpus_summarize()

Summarize the sento_corpus object

ctr_agg()

Set up control for aggregation into sentiment measures

ctr_model()

Set up control for sentiment-based sparse regression modeling

diff(<sento_measures>)

Differencing of sentiment measures

epu

Monthly U.S. Economic Policy Uncertainty index

get_dates()

Get the dates of the sentiment measures/time series

get_dimensions()

Get the dimensions of the sentiment measures

get_hows()

Options supported to perform aggregation into sentiment measures

get_loss_data()

Retrieve loss data from a selection of models

list_lexicons

Built-in lexicons

list_valence_shifters

Built-in valence word lists

measures_fill()

Add and fill missing dates to sentiment measures

measures_update()

Update sentiment measures

merge(<sentiment>)

Merge sentiment objects horizontally and/or vertically

nmeasures()

Get number of sentiment measures

nobs(<sento_measures>)

Get number of observations in the sentiment measures

peakdates()

Extract dates related to sentiment time series peaks

peakdocs()

Extract documents related to sentiment peaks

plot(<attributions>)

Plot prediction attributions at specified level

plot(<sento_measures>)

Plot sentiment measures

plot(<sento_modelIter>)

Plot iterative predictions versus realized values

predict(<sento_model>)

Make predictions from a sento_model object

scale(<sento_measures>)

Scaling and centering of sentiment measures

sentometrics-package

sentometrics: An Integrated Framework for Textual Sentiment Time Series Aggregation and Prediction

sento_corpus()

Create a sento_corpus object

sento_lexicons()

Set up lexicons (and valence word list) for use in sentiment analysis

sento_measures()

One-way road towards a sento_measures object

sento_model()

Optimized and automated sentiment-based sparse regression

subset(<sento_measures>)

Subset sentiment measures

usnews

Texts (not) relevant to the U.S. economy

weights_almon()

Compute Almon polynomials

weights_beta()

Compute Beta weighting curves

weights_exponential()

Compute exponential weighting curves