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#' @useDynLib sentopics,.registration = TRUE
#' @importFrom Rcpp evalCpp sourceCpp
NULL
#' @title Tools for joining sentiment and topic analysis (sentopics)
#'
#' @description **sentopics** provides function to easily estimate a range of
#' topic models and process their output. Particularly, it facilitates the
#' integration of topic analysis with a time dimension through time-series
#' generating functions. In addition, **sentopics** interacts with sentiment
#' analysis to compute the sentiment conveyed by topics. Finally, the package
#' implements a number of visualization helping interpreting the results of
#' topic models.
#'
#' @section Usage:
#'
#' Please refer to the vignettes for a comprehensive introduction to the
#' package functions.
#'
#' - [Basic usage](../doc/Basic_usage.html): Introduction to topic model estimation with **sentopics**
#' - [Topical time series](../doc/Topical_time_series.html): Integrate topic analysis with sentiment analysis along a time dimension
#'
#' For further details, you may browse the package [documentation](../html/00Index.html).
#'
#'
#' @note Please cite the package in publications. Use
#' \code{citation("sentopics")}.
#'
"_PACKAGE"
#' Corpus of press conferences from the European Central Bank
#'
#' @description A corpus of 260 ECB press conference, split into 4224
#' paragraphs. The corpus contains a number of *docvars* indicating the date
#' of the press conference and a measured sentiment based on the
#' Loughran-McDonald lexicon.
#'
#'
#' @examples
#' docvars(ECB_press_conferences)
#'
#' @format A [quanteda::corpus] object.
#' @seealso [ECB_press_conferences_tokens]
#'
#' @source \url{https://www.ecb.europa.eu/press/key/date/html/index.en.html}.
"ECB_press_conferences"
#' Tokenized press conferences
#'
#' @description The pre-processed and tokenized version of the
#' [ECB_press_conferences] corpus of press conferences. The processing
#' involved the following steps:
#'
#' - Subset paragraphs shorter than 10 words
#' - Removal of stop words
#' - Part-of-speech tagging, following which only nouns, proper nouns and
#' adjective were retained.
#' - Detection and merging of frequent compound words
#' - Frequency-based cleaning of rare and very common words
#'
#' @examples
#' LDA(ECB_press_conferences_tokens)
#'
#' @format A [quanteda::tokens] object.
#' @seealso [ECB_press_conferences]
#'
#' @source \url{https://www.ecb.europa.eu/press/key/date/html/index.en.html}.
"ECB_press_conferences_tokens"
#' Loughran-McDonald lexicon
#'
#' @description The Loughran-McDonald lexicon for financial texts adapted for
#' usage in **sentopics**. The lexicon is enhanced with two list of
#' valence-shifting words.
#'
#' @seealso [JST()], [rJST()]
#'
#' @examples
#' JST(ECB_press_conferences_tokens, lexicon = LoughranMcDonald)
#'
#' @format A [quanteda::dictionary] containing two polarity categories (negative
#' and positive) and two valence-shifting categories (negator and amplifier).
#'
#' @source \url{https://sraf.nd.edu/loughranmcdonald-master-dictionary/} for the
#' lexicon and [lexicon::hash_valence_shifters] for the valence shifters.
#'
#' @references Loughran, T. & McDonald, B. (2011). [When Is a Liability Not a
#' Liability? Textual Analysis, Dictionaries, and
#' 10-Ks](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1331573). *The Journal of
#' Finance*, 66(1), 35--65.
"LoughranMcDonald"
#' Picault-Renault lexicon
#'
#' @description The Picault-Renault lexicon, specialized in the analysis of
#' central bank communication. The lexicon identifies a large number of n-grams
#' and gives their probability to belong to six categories:
#'
#' - Monetary Policy - accommodative
#' - Monetary Policy - neutral
#' - Monetary Policy - restrictive
#' - Economic Condition - negative
#' - Economic Condition - neutral
#' - Economic Condition - positive
#'
#' @examples
#' head(PicaultRenault)
#'
#' @format A [data.table] object.
#'
#' @references Picault, M. & Renault, T. (2017). [Words are not all created
#' equal: A new measure of ECB
#' communication](https://www.sciencedirect.com/science/article/abs/pii/S0261560617301808). *Journal of
#' International Money and Finance*, 79, 136--156.
#'
#' @seealso [compute_PicaultRenault_scores()]
#'
#' @source \url{http://www.cbcomindex.com/lexicon.php}
"PicaultRenault"
#' Regression dataset based on Picault & Renault (2017)
#'
#' @description A regression dataset built to partially replicate the result of
#' Picault & Renault. This dataset contains, for each press conference
#' published after 2000:
#'
#' - The Main Refinancing Rate (MRR) of the ECB set following the press
#' conference
#' - The change in the MRR following the press conference
#' - The change in the MRR observed at the previous press conference
#' - The Bloomberg consensus on the announced MRR
#' - The Surprise brought by the announcement, computed as the Bloomberg
#' consensus minus the MRR following the conference
#' - The EURO STOXX 50 return on the day of the press conference
#' - The EURO STOXX 50 return on the day preceding the announcement
#'
#' @examples
#' head(PicaultRenault_data)
#'
#' @format An [xts::xts] object.
#'
#' @references Picault, M. & Renault, T. (2017). [Words are not all created
#' equal: A new measure of ECB
#' communication](https://www.sciencedirect.com/science/article/abs/pii/S0261560617301808). *Journal of
#' International Money and Finance*, 79, 136--156.
#'
#' @source The data was manually prepared by the author of this package.
"PicaultRenault_data"
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