sentopics (version 0.7.4)

sentiment_series: Compute a sentiment time series

Description

Compute a sentiment time series based on the internal sentiment and dates of a sentopicmodel. The time series computation supports multiple sampling period and optionally allow computing a moving average.

Usage

sentiment_series(
  x,
  period = c("year", "quarter", "month", "day"),
  rolling_window = 1,
  scale = TRUE,
  scaling_period = c("1900-01-01", "2099-12-31"),
  as.xts = TRUE,
  ...
)

Value

A time series of sentiment, stored as an xts::xts or data.frame.

Arguments

x

a LDA(), JST() or rJST() model populated with internal dates and/or internal sentiment.

period

the sampling period within which the sentiment of documents will be averaged. period = "identity" is a special case that will return document-level variables before the aggregation happens. Useful to rapidly compute topical sentiment at the document level.

rolling_window

if greater than 1, determines the rolling window to compute a moving average of sentiment. The rolling window is based on the period unit and rely on actual dates (i.e, is not affected by unequally spaced data points).

scale

if TRUE, the resulting time series will be scaled to a mean of zero and a standard deviation of 1. This argument also has the side effect of attaching scaled sentiment values as docvars to the input object with the _scaled suffix.

scaling_period

the date range over which the scaling should be applied. Particularly useful to normalize only the beginning of the time series.

as.xts

if TRUE, returns an xts::xts object. Otherwise, returns a data.frame.

...

other arguments passed on to zoo::rollapply() or mean() and sd().

See Also

sentopics_sentiment sentopics_date

Other series functions: proportion_topics(), sentiment_breakdown(), sentiment_topics()

Examples

lda <- LDA(ECB_press_conferences_tokens)
series <- sentiment_series(lda, period = "month")

# JST and rJST models can use computed sentiment from the sentiment layer,
# but the model must be estimated first.
rjst <- rJST(ECB_press_conferences_tokens, lexicon = LoughranMcDonald)
sentiment_series(rjst)

sentopics_sentiment(rjst) <- NULL ## remove existing sentiment
rjst <- fit(rjst, 10) ## estimating the model is then needed
sentiment_series(rjst)

# note the presence of both raw and scaled sentiment values
# in the initial object
sentopics_sentiment(lda)
sentopics_sentiment(rjst)