Sets up control object for (computation of textual sentiment and) aggregation into textual
sentiment measures.

ctr_agg(
howWithin = "proportional",
howDocs = "equal_weight",
howTime = "equal_weight",
do.sentence = FALSE,
do.ignoreZeros = TRUE,
by = "day",
lag = 1,
fill = "zero",
alphaExpDocs = 0.1,
alphasExp = seq(0.1, 0.5, by = 0.1),
do.inverseExp = FALSE,
ordersAlm = 1:3,
do.inverseAlm = TRUE,
aBeta = 1:4,
bBeta = 1:4,
weights = NULL,
tokens = NULL,
nCore = 1
)

## Arguments

howWithin |
a single `character` vector defining how to perform aggregation within
documents or sentences. Coincides with the `how` argument in the `compute_sentiment` function. Should
`length(howWithin) > 1` , the first element is used. For available options see `get_hows()$words` . |

howDocs |
a single `character` vector defining how aggregation across documents (and/or sentences) per date will
be performed. Should `length(howDocs) > 1` , the first element is used. For available options
see `get_hows()$docs` . |

howTime |
a `character` vector defining how aggregation across dates will be performed. More than one choice
is possible. For available options see `get_hows()$time` . |

do.sentence |
see `compute_sentiment` . |

do.ignoreZeros |
a `logical` indicating whether zero sentiment values have to be ignored in the determination of
the document (and/or sentence) weights while aggregating across documents (and/or sentences). By default
`do.ignoreZeros = TRUE` , such that documents (and/or sentences) with a raw sentiment score of zero or for which
a given feature indicator is equal to zero are considered irrelevant. |

by |
a single `character` vector, either `"day", "week", "month"` or `"year"` , to indicate at what
level the dates should be aggregated. Dates are displayed as the first day of the period, if applicable (e.g.,
`"2017-03-01"` for March 2017). |

lag |
a single `integer` vector, being the time lag to be specified for aggregation across time. By default
equal to `1` , meaning no aggregation across time; a time weighting scheme named `"dummyTime"` is used in
this case. |

fill |
a single `character` vector, one of `c("zero", "latest", "none")` , to control how missing
sentiment values across the continuum of dates considered are added. This impacts the aggregation across time,
applying the `measures_fill` function before aggregating, except if `fill = "none"` . By default equal to
`"zero"` , which sets the scores (and thus also the weights) of the added dates to zero in the time aggregation. |

alphaExpDocs |
a single `integer` vector. A weighting smoothing factor, used if
`"exponential" %in% howDocs` or `"inverseExponential" %in% howDocs` . Value should be between 0 and 1
(both excluded); see `weights_exponential` . |

alphasExp |
a `numeric` vector of all exponential weighting smoothing factors, used if
`"exponential" %in% howTime` . Values should be between 0 and 1 (both excluded); see
`weights_exponential` . |

do.inverseExp |
a `logical` indicating if for every exponential curve its inverse has to be added,
used if `"exponential" %in% howTime` ; see `weights_exponential` . |

ordersAlm |
a `numeric` vector of all Almon polynomial orders (positive) to calculate weights for, used if
`"almon" %in% howTime` ; see `weights_almon` . |

do.inverseAlm |
a `logical` indicating if for every Almon polynomial its inverse has to be added, used
if `"almon" %in% howTime` ; see `weights_almon` . |

aBeta |
a `numeric` vector of positive values as first Beta weighting decay parameter; see
`weights_beta` . |

bBeta |
a `numeric` vector of positive values as second Beta weighting decay parameter; see
`weights_beta` . |

weights |
optional own weighting scheme(s), used if provided as a `data.frame` with the number of rows
equal to the desired `lag` . |

tokens |
see `compute_sentiment` . |

nCore |
see `compute_sentiment` . |

## Value

A `list`

encapsulating the control parameters.

## Details

For available options on how aggregation can occur (via the `howWithin`

,
`howDocs`

and `howTime`

arguments), inspect `get_hows`

. The control parameters
associated to `howDocs`

are used both for aggregation across documents and across sentences.

## See also

## Examples